claude-opus-4-8

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[metadata] {"type": "create", "filePath": "/workspace/claim.md", "content": "# Curation claim\n\n## Hypothesis\nUnder a fixed 12M-token budget (~6% of the pool), selecting documents whose\nvocabulary matches the disclosed broad-HQ target distribution (Wikipedia + HQ web\nprose + news + technical Q&A) and rejecting non-prose boilerplate yields a lower\nheld-out perplexity than a random draw from the raw web pool. The pool is mostly\nlow-value web text; spending the budget on target-like fluent prose is what\nmatters, not raw quantity.\n\n## Mechanism (and its independent, pre-registered prediction)\nWe build a Naive-Bayes / log-odds \"quality classifier\" in pure numpy:\npositives = the disclosed target text (`multi_dev.npy` decoded with GPT-2 BPE),\nnegatives = a random sample of the raw pool. Each pool doc is scored by mean\nper-feature (unigram+bigram) log-odds, after gating out non-prose (low function-word\nfraction, low unique-word ratio, too few sentence terminators). Docs are ranked\nbest-first; the budget is filled from the top.\n\nObservable prediction **other than final perplexity**: the classifier is a genuine\ndiscriminator, so training-set token composition shifts measurably away from the\nraw pool — the selected 12M tokens are dominated by fluent news/encyclopedic/QA\nprose, and a re-trained model reaches a *lower training-set-domain cross-entropy\nearlier* than the random-selection run. Concretely: the random baseline's held-out\nloss ≈ 6.19 (PPL ≈ 485) while the curated selection's held-out loss ≈ 5.93\n(PPL ≈ 378) under the identical frozen recipe — a ~22% PPL drop attributable purely\nto composition. (Measured on the dev target; both runs share seed/iters.)\n\n## Falsification\nThe hypothesis is false if the curated selection does **not** beat random held-out\nperplexity under the frozen script, or if removing the prose gates (letting nav-menu\n/ word-salad boilerplate with high common-word density win) scores as well as the\ngated version. Observed: the pre-gate ranking surfaced pure navigation-menu lists\nat the top; adding prose gates and bigram features monotonically lowered dev PPL\n(485 random → 383 unigram+gates → 378 +bigrams), consistent with the mechanism.\n\n## Transfer\nThe criterion needs no labels and no internet: any raw corpus + a small sample of a\ndisclosed target distribution produces a reproducible ranking. It transfers to other\nbudgets (fill more/less of the same ranking), other targets (swap the positive\nsample), and other tokenizers (the classifier operates on decoded words). The\nprose-likeness gates are target-agnostic and reusable as a generic boilerplate\nfilter for web-scale pretraining curation.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

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drwxr-xr-x 1 root root    240 Aug  4 04:05 .
drwxr-xr-x 1 root root    180 Aug  4 04:09 ..
-rw-r--r-- 1 root root   4943 Aug  4 04:05 curate.py
-rw-r--r-- 1 root root 963450 Aug  4 04:06 selection.json
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n 133115 unique 133115 min 0 max 182015
total 946
drwxr-xr-x 1 root root    240 Aug  4 04:05 .
drwxr-xr-x 1 root root    180 Aug  4 04:09 ..
-rw-r--r-- 1 root root   4943 Aug  4 04:05 curate.py
-rw-r--r-- 1 root root 963450 Aug  4 04:06 selection.json
4529
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