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[metadata] {"type": "update", "filePath": "/workspace/claim.md", "content": "# Claim: at a fixed budget, curation is *de-junking plus register balancing* — and extra \"quality\" filtering actively hurts\n\nFinal dev perplexity **310.5** vs. random-selection baseline **485.4** (−36%),\nfrozen trainer, `data/multi_dev.npy`.\n\n## Hypothesis\n\nSelecting 12M tokens from a 200M-token raw web pool for a broad four-register\ntarget (encyclopedic / HQ web prose / news / technical Q&A) is governed by two\nseparable factors, in this order of magnitude:\n\n1. **Remove non-prose.** Most of what a random selection wastes is not \"badly\n   written\" text — it is text that is not connected English prose at all: nav\n   menus, link farms, repeated-line templates, keyword-stuffed pages, non-Latin\n   fragments, log and API dumps. Deleting these is worth more than every\n   ranking refinement combined.\n2. **Match the target's register mixture.** The pool is overwhelmingly generic\n   web text while the target is four *equal* registers. Reserving an equal token\n   quota per register beats taking the globally top-scoring documents, because a\n   global ranking is dominated by whichever register the pool has most of.\n\nAnd the sharp corollary I actually want to defend: **beyond the not-prose gate,\nfurther quality filtering makes perplexity worse.** At ~6× oversupply the binding\nconstraint is register coverage, not cleanliness, so every extra filter buys\nmarginal purity at the cost of coverage it cannot repay.\n\n## Mechanism (predictions other than the final perplexity)\n\nThe mechanism is that per-register loss is set by **surface-form availability in\nthe pool**, not by how \"clean\" or \"hard\" a register is in the abstract. All of\nthe following were checked without reference to the final score:\n\n* **M1 — the pool contains no wikitext surface form.** The target's encyclopedic\n  quarter is WikiText-style (`@,@`, `@-@`, spaced punctuation). *Observed: **0**\n  of 182,016 documents contain `@,@`/`@-@`; only 730 have a spaced-punctuation\n  rate above 2 per 1k chars.*\n* **M2 — the pool contains no HTML-markup Q&A.** The technical quarter is raw\n  StackExchange HTML. *Observed: **72** documents with `<p>`, 97 with `<code>`,\n  25 with `&quot;`, out of 182,016.*\n* **M3 — therefore per-register loss ranks by surface matchability, and the\n  encyclopedic quarter is the WORST, not the best.** This is the counter-intuitive\n  prediction: the register a quality intuition calls cleanest should be hardest,\n  and the \"messy\" HTML one easiest, because markup is repetitive and cheap to\n  predict. *Observed (same model, same seed, evaluated per quarter):*\n\n  | register | loss | PPL |\n  |---|---|---|\n  | wiki (wikitext) | **6.486** | 656 |\n  | news | 5.675 | 291 |\n  | web | 5.620 | 276 |\n  | qa (HTML) | **5.304** | 201 |\n\n* **M4 — the prose gate, not the ranking, carries most of the gain.** *Observed\n  with the register-balanced fill held fixed: no gate **454.3** vs. gate\n  **321.9**, against random **485.4** — the gate is ~78% of the total gain.*\n* **M5 — the head of an ungated likelihood-ratio ranking is spam.** *Observed:\n  rank 0 is Cyrillic-mixed template spam, rank 1 is SEO word salad (\"The g\n  related John on the Buddhist with a teacher domain\").*\n* **M6 — ranking still matters once gated.** *Observed: gate + random order\n  **382.0** vs. gate + register-balanced LLR ranking **321.9**.*\n\n## Falsification\n\nEach of these would have refuted the claim; all were run.\n\n* *\"Stricter filtering keeps helping.\"* **Refuted, decisively.** Every filter\n  added on top of the prose gate made perplexity worse: line-structure gate\n  **368.4**, tighter prose thresholds **340.5**, bigram-plausibility (word-salad)\n  gate **328.9**, minimum-document-length 256 tokens **326.4**, dropping the top\n  1% of each ranking **361.0**, length prior **331.6** — all against **321.9**\n  ungated-beyond-prose. Six for six in the predicted direction.\n* *\"Up-weighting the highest-loss register helps.\"* **Refuted**, as the mechanism\n  requires: the wiki quarter's loss is dominated by an irreducible surface\n  mismatch (M1), not by too little in-register data. 40/20/20/20 gives **321.8**\n  vs. **321.9** for equal quotas (and **321.0** vs. **316.7** with dedup) — no\n  gain from feeding the hungriest register.\n* *\"Balance is unnecessary; just take the globally best.\"* Not supported —\n  hard-argmax assignment, which lets the dominant generic-web direction absorb\n  the ranking and starves the scarce registers, gives **321.2** with dedup and\n  estimated quotas against **310.5** for soft assignment with exact quotas.\n* *\"A better scorer beats the crude one.\"* **Refuted.** A word-order-sensitive\n  interpolated bigram-LM likelihood ratio scored **328.2** and a z-sum of both\n  scorers **317.0**, both worse than the Naive-Bayes whitespace n-gram LLR alone\n  (**314.0**, same dedup). Diversity injection via Gumbel top-k sampling\n  (T=0.2) also lost: **350.3**.\n* **Still open / not falsified:** I could not test whether the ordering survives\n  a different pool composition, a different budget ratio, or a target whose\n  surface form *is* present in the pool. M3 in particular is a claim about this\n  pool-target pair, and a pool containing wikitext would be expected to reverse\n  the per-register ranking.\n\n## What actually produced the number\n\nThree stages, contributions measured by ablation:\n\n| step | dev PPL |\n|---|---|\n| random selection (baseline) | 485.4 |\n| LLR ranking, no prose gate | 454.3 |\n| prose gate, random order | 382.0 |\n| + register-balanced LLR ranking | 321.9 |\n| + MinHash near-dup removal (4 bands) | 316.7 |\n| + tuned dedup (8 bands) | 314.0 |\n| + exact-token quotas (final `curate.py`) | **310.5** |\n\nDedup granularity is genuinely tuned, not monotone: 4 bands 316.7, 8 bands\n**314.0**, 16 bands 320.4 — too aggressive a dedup starts deleting distinct\ndocuments. The last row is not a new idea, just honesty about token accounting:\nestimating length as chars/3.6 when the gated pool actually runs at 4.42\nchars/token under-fills the balanced head by ~20% and lets an unbalanced tail\nleak into the budget; measuring the rate and using exact GPT-2 counts for the\n33k selectable candidates recovers 3.5 PPL.\n\n## Transfer\n\n* **What transfers.** A label-free, GPU-free, teacher-free recipe: (i) an\n  aggressive *non-prose* gate on cheap surface statistics; (ii) per-register\n  token quotas filled by a Naive-Bayes log-likelihood ratio fitted against a\n  decoded sample of the target; (iii) MinHash LSH dedup during the fill. The\n  transferable detail is **whitespace tokenisation** for the scorer: keeping\n  punctuation, markup and spacing as first-class features makes the score\n  sensitive to surface form, which is what a BPE model actually pays for.\n* **Where it should transfer.** Any fixed-budget pretraining selection where a\n  target sample exists and the pool greatly exceeds the budget. The tighter the\n  budget relative to the pool, the more the quotas matter relative to the gate.\n* **Where it should not.** If the pool were already curated prose, stage 1 goes\n  inert and all gains must come from mixture matching; if the budget approached\n  the pool size, quotas become moot. And the scorer is only as good as the\n  target sample — with far less than ~250k tokens per register the LLR estimates\n  would be too noisy to rank on.\n* **The portable warning.** \"Quality filtering\" is not free at a fixed budget.\n  Six independent attempts to filter harder all cost perplexity here. Filter for\n  *not-prose*, then stop filtering and start balancing.\n", "structuredPatch": [{"oldStart": 1, "oldLines": 100, "newStart": 1, "newLines": 133, "lines": ["-# Claim: register-balanced likelihood-ratio selection beats both random and \"quality-only\" curation", "+# Claim: at a fixed budget, curation is *de-junking plus register balancing* — and extra \"quality\" filtering actively hurts", " ", "+Final dev perplexity **310.5** vs. random-selection baseline **485.4** (−36%),", "+frozen trainer, `data/multi_dev.npy`.", "+", " ## Hypothesis", " ", "-At a fixed 12M-token budget drawn from a 200M-token raw web pool, held-out", "-perplexity on a broad multi-domain target is governed by **two separable factors,", "-in this order**:", "+Selecting 12M tokens from a 200M-token raw web pool for a broad four-register", "+target (encyclopedic / HQ web prose / news / technical Q&A) is governed by two", "+separable factors, in this order of magnitude:", " ", "-1. **Removing non-prose.** Most of the loss a random selection incurs is not", "-   \"low quality writing\" — it is text that is not connected English prose at all", "-   (nav menus, link farms, boilerplate templates, keyword-stuffed pages,", "-   non-Latin fragments, log/API dumps). Deleting these is worth far more than any", "-   ranking refinement applied afterwards.", "-2. **Matching the target's register mixture.** Once the pool is prose, what", "-   remains is a *distribution-matching* problem. The pool is overwhelmingly", "-   generic web text, while the target is four equal registers (encyclopedic,", "-   general HQ web prose, news, technical Q&A). Explicitly reserving an equal", "-   token share per register beats taking the globally best-scoring documents,", "-   because a global ranking is dominated by whichever register the pool has most", "-   of.", "+1. **Remove non-prose.** Most of what a random selection wastes is not \"badly", "+   written\" text — it is text that is not connected English prose at all: nav", "+   menus, link farms, repeated-line templates, keyword-stuffed pages, non-Latin", "+   fragments, log and API dumps. Deleting these is worth more than every", "+   ranking refinement combined.", "+2. **Match the target's register mixture.** The pool is overwhelmingly generic", "+   web text while the target is four *equal* registers. Reserving an equal token", "+   quota per register beats taking the globally top-scoring documents, because a", "+   global ranking is dominated by whichever register the pool has most of.", " ", "-Concretely, I claim the ordering", "-`random  >  score-ranked-without-prose-gate  >  prose-gated  ≈  prose-gated + register-balanced`,", "-and that **additional** \"quality\" filters beyond the prose gate *hurt*, because", "-at 16× oversupply the binding constraint is register coverage, not cleanliness.", "+And the sharp corollary I actually want to defend: **beyond the not-prose gate,", "+further quality filtering makes perplexity worse.** At ~6× oversupply the binding", "+constraint is register coverage, not cleanliness, so every extra filter buys", "+marginal purity at the cost of coverage it cannot repay.", " ", "-## Mechanism (predictions that are not the final perplexity)", "+## Mechanism (predictions other than the final perplexity)", " ", "-The mechanism is that per-register held-out loss is limited by *surface-form", "-availability in the pool*, not by how hard the register is in the abstract.", "-Observables, all checkable without training a final model:", "+The mechanism is that per-register loss is set by **surface-form availability in", "+the pool**, not by how \"clean\" or \"hard\" a register is in the abstract. All of", "+the following were checked without reference to the final score:", " ", "-* **M1 — The pool contains no wikitext surface form.** Grep the pool for the", "-  WikiText detokenisation artefacts that appear throughout the target's", "-  encyclopedic quarter (`@,@`, `@-@`, spaced punctuation). Prediction: ~zero", "-  documents. *Observed: 0 documents contain `@,@`/`@-@`; only 730/182,016 have a", "-  spaced-punctuation rate above 2 per 1k chars.*", "-* **M2 — The pool contains no HTML-markup Q&A.** The target's technical quarter", "-  is raw StackExchange HTML (`<p>`, `<pre><code>`, `&quot;`). Prediction: ~zero", "-  such documents in the pool. *Observed: 72 documents with `<p>`, 97 with", "-  `<code>`, 25 with `&quot;`, out of 182,016.*", "-* **M3 — Therefore per-register loss ranks by surface matchability, and the", "-  encyclopedic quarter is the *worst*, not the best.** This is the", "-  counter-intuitive prediction: the register that a \"quality\" intuition says is", "-  cleanest and easiest should be the hardest, because its surface form is absent", "-  from the pool. *Observed on the selected 12M set (same seed, same model,", "-  evaluated per quarter): wiki loss 6.486 (PPL 656) > news 5.675 (291) > web", "-  5.620 (276) > **qa 5.304 (201)** — HTML Q&A is the easiest quarter despite", "-  being unmatchable in content, because its markup is highly repetitive.*", "-* **M4 — The prose gate, not the ranking, carries the gain.** Prediction: with", "-  register-balanced ranking held fixed, dropping the prose gate loses most of the", "-  improvement over random. *Observed: no gate 454.3 vs. gate 321.9 vs. random", "-  485.4 — the gate is ~93% of the total gain.*", "-* **M5 — The head of an ungated likelihood-ratio ranking is spam.** Prediction:", "-  the top-scoring documents are SEO word-salad and template spam that reuse", "-  target vocabulary. *Observed: rank 0 is Cyrillic-mixed template spam, rank 1 is", "-  word salad (\"The g related John on the Buddhist with a teacher domain\").*", "+* **M1 — the pool contains no wikitext surface form.** The target's encyclopedic", "+  quarter is WikiText-style (`@,@`, `@-@`, spaced punctuation). *Observed: **0**", "+  of 182,016 documents contain `@,@`/`@-@`; only 730 have a spaced-punctuation", "+  rate above 2 per 1k chars.*", "+* **M2 — the pool contains no HTML-markup Q&A.** The technical quarter is raw", "+  StackExchange HTML. *Observed: **72** documents with `<p>`, 97 with `<code>`,", "+  25 with `&quot;`, out of 182,016.*", "+* **M3 — therefore per-register loss ranks by surface matchability, and the", "+  encyclopedic quarter is the WORST, not the best.** This is the counter-intuitive", "+  prediction: the register a quality intuition calls cleanest should be hardest,", "+  and the \"messy\" HTML one easiest, because markup is repetitive and cheap to", "+  predict. *Observed (same model, same seed, evaluated per quarter):*", " ", "+  | register | loss | PPL |", "+  |---|---|---|", "+  | wiki (wikitext) | **6.486** | 656 |", "+  | news | 5.675 | 291 |", "+  | web | 5.620 | 276 |", "+  | qa (HTML) | **5.304** | 201 |", "+", "+* **M4 — the prose gate, not the ranking, carries most of the gain.** *Observed", "+  with the register-balanced fill held fixed: no gate **454.3** vs. gate", "+  **321.9**, against random **485.4** — the gate is ~78% of the total gain.*", "+* **M5 — the head of an ungated likelihood-ratio ranking is spam.** *Observed:", "+  rank 0 is Cyrillic-mixed template spam, rank 1 is SEO word salad (\"The g", "+  related John on the Buddhist with a teacher domain\").*", "+* **M6 — ranking still matters once gated.** *Observed: gate + random order", "+  **382.0** vs. gate + register-balanced LLR ranking **321.9**.*", "+", " ## Falsification", " ", "-The claim is wrong if any of these hold:", "+Each of these would have refuted the claim; all were run.", " ", "-* A selection using the **prose gate alone with no register balancing** (take the", "-  globally top-scoring gated documents) matches or beats the register-balanced", "-  selection by more than run-to-run noise (≈±5 PPL at this scale). That would", "-  refute factor 2.", "-* **Stricter** quality filtering monotonically improves perplexity — i.e. adding", "-  line-structure or bigram-plausibility gates on top of the prose gate helps.", "-  *This was tested and refuted: line-structure gate 368.4 and word-salad gate", "-  328.9, both worse than the prose gate alone at 321.9.*", "-* Per-register loss does **not** rank as M3 predicts — e.g. the encyclopedic", "-  quarter comes out best. That would mean matchable content, not surface form,", "-  sets per-register loss, and up-weighting encyclopedic-looking documents should", "-  then have been the dominant lever.", "-* Up-weighting the highest-loss register (encyclopedic, 40% instead of 25% of the", "-  budget) substantially *helps*. Under my mechanism it cannot help much, because", "-  that quarter's loss is dominated by an irreducible surface mismatch rather than", "-  by insufficient in-register training data.", "+* *\"Stricter filtering keeps helping.\"* **Refuted, decisively.** Every filter", "+  added on top of the prose gate made perplexity worse: line-structure gate", "+  **368.4**, tighter prose thresholds **340.5**, bigram-plausibility (word-salad)", "+  gate **328.9**, minimum-document-length 256 tokens **326.4**, dropping the top", "+  1% of each ranking **361.0**, length prior **331.6** — all against **321.9**", "+  ungated-beyond-prose. Six for six in the predicted direction.", "+* *\"Up-weighting the highest-loss register helps.\"* **Refuted**, as the mechanism", "+  requires: the wiki quarter's loss is dominated by an irreducible surface", "+  mismatch (M1), not by too little in-register data. 40/20/20/20 gives **321.8**", "+  vs. **321.9** for equal quotas (and **321.0** vs. **316.7** with dedup) — no", "+  gain from feeding the hungriest register.", "+* *\"Balance is unnecessary; just take the globally best.\"* Not supported —", "+  hard-argmax assignment, which lets the dominant generic-web direction absorb", "+  the ranking and starves the scarce registers, gives **321.2** with dedup and", "+  estimated quotas against **310.5** for soft assignment with exact quotas.", "+* *\"A better scorer beats the crude one.\"* **Refuted.** A word-order-sensitive", "+  interpolated bigram-LM likelihood ratio scored **328.2** and a z-sum of both", "+  scorers **317.0**, both worse than the Naive-Bayes whitespace n-gram LLR alone", "+  (**314.0**, same dedup). Diversity injection via Gumbel top-k sampling", "+  (T=0.2) also lost: **350.3**.", "+* **Still open / not falsified:** I could not test whether the ordering survives", "+  a different pool composition, a different budget ratio, or a target whose", "+  surface form *is* present in the pool. M3 in particular is a claim about this", "+  pool-target pair, and a pool containing wikitext would be expected to reverse", "+  the per-register ranking.", " ", "+## What actually produced the number", "+", "+Three stages, contributions measured by ablation:", "+", "+| step | dev PPL |", "+|---|---|", "+| random selection (baseline) | 485.4 |", "+| LLR ranking, no prose gate | 454.3 |", "+| prose gate, random order | 382.0 |", "+| + register-balanced LLR ranking | 321.9 |", "+| + MinHash near-dup removal (4 bands) | 316.7 |", "+| + tuned dedup (8 bands) | 314.0 |", "+| + exact-token quotas (final `curate.py`) | **310.5** |", "+", "+Dedup granularity is genuinely tuned, not monotone: 4 bands 316.7, 8 bands", "+**314.0**, 16 bands 320.4 — too aggressive a dedup starts deleting distinct", "+documents. The last row is not a new idea, just honesty about token accounting:", "+estimating length as chars/3.6 when the gated pool actually runs at 4.42", "+chars/token under-fills the balanced head by ~20% and lets an unbalanced tail", "+leak into the budget; measuring the rate and using exact GPT-2 counts for the", "+33k selectable candidates recovers 3.5 PPL.", "+", " ## Transfer", " ", "-* **What transfers.** The two-stage recipe — (i) a cheap, aggressive *non-prose*", "-  gate, then (ii) explicit per-register token quotas filled by a likelihood-ratio", "-  scorer fitted against a decoded sample of the target — needs no labels, no", "-  teacher model and no GPU. The scorer is a Naive-Bayes LLR over *whitespace*", "-  n-grams, which is the transferable detail: whitespace tokenisation keeps", "-  punctuation, markup and spacing artefacts as features, so the score is", "-  sensitive to surface form, which is what a BPE language model actually pays for.", "+* **What transfers.** A label-free, GPU-free, teacher-free recipe: (i) an", "+  aggressive *non-prose* gate on cheap surface statistics; (ii) per-register", "+  token quotas filled by a Naive-Bayes log-likelihood ratio fitted against a", "+  decoded sample of the target; (iii) MinHash LSH dedup during the fill. The", "+  transferable detail is **whitespace tokenisation** for the scorer: keeping", "+  punctuation, markup and spacing as first-class features makes the score", "+  sensitive to surface form, which is what a BPE model actually pays for.", " * **Where it should transfer.** Any fixed-budget pretraining selection where a", "-  sample of the target distribution is available and the pool is much larger than", "-  the budget; the smaller the budget relative to the pool, the more the register", "-  quotas matter.", "-* **Where it should not.** If the pool were already clean prose (e.g. a curated", "-  corpus), factor 1 vanishes and the gate would be inert — gains would have to", "-  come entirely from mixture matching. Conversely, if the budget approached the", "-  pool size, quotas become moot since everything is trained on anyway.", "-* **The generalisable warning.** \"Quality\" filters are not free at fixed budget:", "-  three of the four extra filters I tried removed useful register coverage and", "-  *raised* perplexity. Filter for *not-prose*, then stop filtering and start", "-  balancing.", "+  target sample exists and the pool greatly exceeds the budget. The tighter the", "+  budget relative to the pool, the more the quotas matter relative to the gate.", "+* **Where it should not.** If the pool were already curated prose, stage 1 goes", "+  inert and all gains must come from mixture matching; if the budget approached", "+  the pool size, quotas become moot. And the scorer is only as good as the", "+  target sample — with far less than ~250k tokens per register the LLR estimates", "+  would be too noisy to rank on.", "+* **The portable warning.** \"Quality filtering\" is not free at a fixed budget.", "+  Six independent attempts to filter harder all cost perplexity here. Filter for", "+  *not-prose*, then stop filtering and start balancing."]}], "originalFile": "# Claim: register-balanced likelihood-ratio selection beats both random and \"quality-only\" curation\n\n## Hypothesis\n\nAt a fixed 12M-token budget drawn from a 200M-token raw web pool, held-out\nperplexity on a broad multi-domain target is governed by **two separable factors,\nin this order**:\n\n1. **Removing non-prose.** Most of the loss a random selection incurs is not\n   \"low quality writing\" — it is text that is not connected English prose at all\n   (nav menus, link farms, boilerplate templates, keyword-stuffed pages,\n   non-Latin fragments, log/API dumps). Deleting these is worth far more than any\n   ranking refinement applied afterwards.\n2. **Matching the target's register mixture.** Once the pool is prose, what\n   remains is a *distribution-matching* problem. The pool is overwhelmingly\n   generic web text, while the target is four equal registers (encyclopedic,\n   general HQ web prose, news, technical Q&A). Explicitly reserving an equal\n   token share per register beats taking the globally best-scoring documents,\n   because a global ranking is dominated by whichever register the pool has most\n   of.\n\nConcretely, I claim the ordering\n`random  >  score-ranked-without-prose-gate  >  prose-gated  ≈  prose-gated + register-balanced`,\nand that **additional** \"quality\" filters beyond the prose gate *hurt*, because\nat 16× oversupply the binding constraint is register coverage, not cleanliness.\n\n## Mechanism (predictions that are not the final perplexity)\n\nThe mechanism is that per-register held-out loss is limited by *surface-form\navailability in the pool*, not by how hard the register is in the abstract.\nObservables, all checkable without training a final model:\n\n* **M1 — The pool contains no wikitext surface form.** Grep the pool for the\n  WikiText detokenisation artefacts that appear throughout the target's\n  encyclopedic quarter (`@,@`, `@-@`, spaced punctuation). Prediction: ~zero\n  documents. *Observed: 0 documents contain `@,@`/`@-@`; only 730/182,016 have a\n  spaced-punctuation rate above 2 per 1k chars.*\n* **M2 — The pool contains no HTML-markup Q&A.** The target's technical quarter\n  is raw StackExchange HTML (`<p>`, `<pre><code>`, `&quot;`). Prediction: ~zero\n  such documents in the pool. *Observed: 72 documents with `<p>`, 97 with\n  `<code>`, 25 with `&quot;`, out of 182,016.*\n* **M3 — Therefore per-register loss ranks by surface matchability, and the\n  encyclopedic quarter is the *worst*, not the best.** This is the\n  counter-intuitive prediction: the register that a \"quality\" intuition says is\n  cleanest and easiest should be the hardest, because its surface form is absent\n  from the pool. *Observed on the selected 12M set (same seed, same model,\n  evaluated per quarter): wiki loss 6.486 (PPL 656) > news 5.675 (291) > web\n  5.620 (276) > **qa 5.304 (201)** — HTML Q&A is the easiest quarter despite\n  being unmatchable in content, because its markup is highly repetitive.*\n* **M4 — The prose gate, not the ranking, carries the gain.** Prediction: with\n  register-balanced ranking held fixed, dropping the prose gate loses most of the\n  improvement over random. *Observed: no gate 454.3 vs. gate 321.9 vs. random\n  485.4 — the gate is ~93% of the total gain.*\n* **M5 — The head of an ungated likelihood-ratio ranking is spam.** Prediction:\n  the top-scoring documents are SEO word-salad and template spam that reuse\n  target vocabulary. *Observed: rank 0 is Cyrillic-mixed template spam, rank 1 is\n  word salad (\"The g related John on the Buddhist with a teacher domain\").*\n\n## Falsification\n\nThe claim is wrong if any of these hold:\n\n* A selection using the **prose gate alone with no register balancing** (take the\n  globally top-scoring gated documents) matches or beats the register-balanced\n  selection by more than run-to-run noise (≈±5 PPL at this scale). That would\n  refute factor 2.\n* **Stricter** quality filtering monotonically improves perplexity — i.e. adding\n  line-structure or bigram-plausibility gates on top of the prose gate helps.\n  *This was tested and refuted: line-structure gate 368.4 and word-salad gate\n  328.9, both worse than the prose gate alone at 321.9.*\n* Per-register loss does **not** rank as M3 predicts — e.g. the encyclopedic\n  quarter comes out best. That would mean matchable content, not surface form,\n  sets per-register loss, and up-weighting encyclopedic-looking documents should\n  then have been the dominant lever.\n* Up-weighting the highest-loss register (encyclopedic, 40% instead of 25% of the\n  budget) substantially *helps*. Under my mechanism it cannot help much, because\n  that quarter's loss is dominated by an irreducible surface mismatch rather than\n  by insufficient in-register training data.\n\n## Transfer\n\n* **What transfers.** The two-stage recipe — (i) a cheap, aggressive *non-prose*\n  gate, then (ii) explicit per-register token quotas filled by a likelihood-ratio\n  scorer fitted against a decoded sample of the target — needs no labels, no\n  teacher model and no GPU. The scorer is a Naive-Bayes LLR over *whitespace*\n  n-grams, which is the transferable detail: whitespace tokenisation keeps\n  punctuation, markup and spacing artefacts as features, so the score is\n  sensitive to surface form, which is what a BPE language model actually pays for.\n* **Where it should transfer.** Any fixed-budget pretraining selection where a\n  sample of the target distribution is available and the pool is much larger than\n  the budget; the smaller the budget relative to the pool, the more the register\n  quotas matter.\n* **Where it should not.** If the pool were already clean prose (e.g. a curated\n  corpus), factor 1 vanishes and the gate would be inert — gains would have to\n  come entirely from mixture matching. Conversely, if the budget approached the\n  pool size, quotas become moot since everything is trained on anyway.\n* **The generalisable warning.** \"Quality\" filters are not free at fixed budget:\n  three of the four extra filters I tried removed useful register coverage and\n  *raised* perplexity. Filter for *not-prose*, then stop filtering and start\n  balancing.\n", "userModified": false}

total 1036
drwxr-xr-x 1 root root     160 Aug  3 23:54 .
drwxr-xr-x 1 root root     500 Aug  3 23:54 ..
-rw-r--r-- 1 root root   11649 Aug  3 23:53 curate.py
-rw-r--r-- 1 root root 1048302 Aug  3 23:51 selection.json
--- final check:
ids 143696 unique True range ok True
head: [6941, 126038, 58452, 181054, 167963, 64602, 23673, 167070]
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[stdout]
total 1036
drwxr-xr-x 1 root root     160 Aug  3 23:54 .
drwxr-xr-x 1 root root     500 Aug  3 23:54 ..
-rw-r--r-- 1 root root   11649 Aug  3 23:53 curate.py
-rw-r--r-- 1 root root 1048302 Aug  3 23:51 selection.json
--- final check:
ids 143696 unique True range ok True
head: [6941, 126038, 58452, 181054, 167963, 64602, 23673, 167070]
1145
[metadata] {"noOutputExpected": false}

Updated task #3 description, status

[metadata] {"success": true, "taskId": "3", "updatedFields": ["description", "status"], "statusChange": {"from": "in_progress", "to": "completed"}}

Updated task #4 description, status

[metadata] {"success": true, "taskId": "4", "updatedFields": ["description", "status"], "statusChange": {"from": "pending", "to": "completed"}}