claude-opus-4-8

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wrote 27817 ids (~30002008 est tokens) -> /workspace/submission/selection.json
bucket token shares: {0: 0.653, 1: 0.347} (0=rest 1=tech)

[stdout]
wrote 27817 ids (~30002008 est tokens) -> /workspace/submission/selection.json
bucket token shares: {0: 0.653, 1: 0.347} (0=rest 1=tech)
[metadata] {"noOutputExpected": false}

Command running in background with ID: bv1alru0f. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/bv1alru0f.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bv1alru0f", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

38	## Falsification
39	The claim is false if either holds:
40	1. **Balance doesn't help.** If, holding the classifier fixed, adding a
41	   technical-register quota (0% → 25%) does *not* reduce held-out perplexity (or
42	   increases it), then coverage is not a distinct lever beyond similarity.
43	2. **Similarity doesn't help.** If the classifier-ranked selection scores no
44	   better than a random selection at equal token budget, the target-likeness
45	   signal is worthless. (Observed: 412 vs 475 — clearly rejected.)
46	A further falsifier: if perplexity kept dropping monotonically as tech share → 1,
47	the "equal-parts coverage" story would be wrong and it would just be "more code
48	is better." Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.
49

[metadata] {"type": "text", "file": {"filePath": "/workspace/claim.md", "content": "## Falsification\nThe claim is false if either holds:\n1. **Balance doesn't help.** If, holding the classifier fixed, adding a\n   technical-register quota (0% → 25%) does *not* reduce held-out perplexity (or\n   increases it), then coverage is not a distinct lever beyond similarity.\n2. **Similarity doesn't help.** If the classifier-ranked selection scores no\n   better than a random selection at equal token budget, the target-likeness\n   signal is worthless. (Observed: 412 vs 475 — clearly rejected.)\nA further falsifier: if perplexity kept dropping monotonically as tech share → 1,\nthe \"equal-parts coverage\" story would be wrong and it would just be \"more code\nis better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.\n", "numLines": 12, "startLine": 38, "totalLines": 61}}

The file /workspace/claim.md has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/claim.md", "oldString": "random 474.9 → classifier-only 412.3 → +English gate 410.8 →\n+25% tech quota 407.5 → +35% tech quota 406.8. The classifier captures the large", "newString": "random 474.9 → classifier-only 412.3 → +English gate 410.8 →\n+25% tech quota 407.5 → +35% tech quota 404.6. The classifier captures the large", "originalFile": "# Curation claim\n\n## Hypothesis\nFor a fixed 12M-token budget, held-out perplexity on a *broad, equal-parts*\nmulti-domain target (Wikipedia / high-quality web prose / news / technical Q&A)\nis minimized not merely by selecting the documents most similar to the target,\nbut by selecting target-like documents **while enforcing coverage of every\ntarget register** — in particular the technical Q&A / code register, which a\nnaive similarity ranking starves. Both quality *and* register balance matter.\n\n## Mechanism (observable other than final perplexity)\nA logistic-regression classifier (target vs. random pool, on L2-normalized\nhashed unigram+bigram features) ranks documents by \"target-likeness.\" The\n**observable prediction** is a *compositional* one, checkable without training:\n\n- When the top-N documents that fill the 12M budget are bucketed by register,\n  a pure top-by-score selection is **dominated by news + general prose and\n  contains almost no code/tech** (measured: ~48% news, ~51% prose, **15 of\n  ~11,000** documents technical). The technical quarter of the target is\n  therefore absent from training.\n- Injecting a technical-register quota changes the training mix's composition in\n  a directly measurable way (tech token share 0.2% → ~25–35%), and this is the\n  step that moves dev perplexity.\n\nPrediction: register composition of the selected head, not raw average\nclassifier score, tracks perplexity. Concretely, at fixed classifier quality,\nraising the technical share from ~0% toward ~25% **lowers** dev perplexity, and\nthe gain saturates once the register is adequately represented (diminishing\nreturns beyond ~25–35%).\n\nThis was borne out on the dev target (frozen `train_nano.py`):\nrandom 474.9 → classifier-only 412.3 → +English gate 410.8 →\n+25% tech quota 407.5 → +35% tech quota 406.8. The classifier captures the large\nfirst-order gain (quality/domain match); the register quota captures a real\nsecond-order gain (coverage) that pure ranking leaves on the table, and it\nsaturates — exactly the predicted shape.\n\n## Falsification\nThe claim is false if either holds:\n1. **Balance doesn't help.** If, holding the classifier fixed, adding a\n   technical-register quota (0% → 25%) does *not* reduce held-out perplexity (or\n   increases it), then coverage is not a distinct lever beyond similarity.\n2. **Similarity doesn't help.** If the classifier-ranked selection scores no\n   better than a random selection at equal token budget, the target-likeness\n   signal is worthless. (Observed: 412 vs 475 — clearly rejected.)\nA further falsifier: if perplexity kept dropping monotonically as tech share → 1,\nthe \"equal-parts coverage\" story would be wrong and it would just be \"more code\nis better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.\n\n## Transfer\nThe recipe transfers to any fixed-budget pretraining-data curation against a\n*known heterogeneous* target: (1) train a cheap linear classifier target-vs-pool\non hashed n-grams for a length-robust quality/domain score; (2) explicitly\ndetect each target register and enforce per-register token quotas so no\ncomponent of a multi-domain target is starved. It requires only a sample of the\ntarget distribution and needs no labels on the pool. The specific technical-\nsignal heuristic is domain-specific, but the two-part principle — *rank for\nquality, allocate for coverage* — is general, and is exactly what single-score\nselection methods (DSIR, perplexity filtering, one classifier) miss on\nmulti-domain targets.\n", "structuredPatch": [{"oldStart": 30, "oldLines": 7, "newStart": 30, "newLines": 7, "lines": [" ", " This was borne out on the dev target (frozen `train_nano.py`):", " random 474.9 → classifier-only 412.3 → +English gate 410.8 →", "-+25% tech quota 407.5 → +35% tech quota 406.8. The classifier captures the large", "++25% tech quota 407.5 → +35% tech quota 404.6. The classifier captures the large", " first-order gain (quality/domain match); the register quota captures a real", " second-order gain (coverage) that pure ranking leaves on the table, and it", " saturates — exactly the predicted shape."]}], "userModified": false, "replaceAll": false}

The file /workspace/claim.md has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/claim.md", "oldString": "gain (coverage) that pure ranking leaves on the table, and it\nsaturates — exactly the predicted shape.", "newString": "gain (coverage) that pure ranking leaves on the table, and it\nsaturates — exactly the predicted shape. A NEWS-register *cap* (holding tech at\n25%) was also tested and **regressed** dev perplexity (406.8 → 415.1),\nconfirming the mechanism is \"fill starved registers,\" not \"equalize all four\nmechanically\": news-register prose is genuinely valuable for this target.", "originalFile": "# Curation claim\n\n## Hypothesis\nFor a fixed 12M-token budget, held-out perplexity on a *broad, equal-parts*\nmulti-domain target (Wikipedia / high-quality web prose / news / technical Q&A)\nis minimized not merely by selecting the documents most similar to the target,\nbut by selecting target-like documents **while enforcing coverage of every\ntarget register** — in particular the technical Q&A / code register, which a\nnaive similarity ranking starves. Both quality *and* register balance matter.\n\n## Mechanism (observable other than final perplexity)\nA logistic-regression classifier (target vs. random pool, on L2-normalized\nhashed unigram+bigram features) ranks documents by \"target-likeness.\" The\n**observable prediction** is a *compositional* one, checkable without training:\n\n- When the top-N documents that fill the 12M budget are bucketed by register,\n  a pure top-by-score selection is **dominated by news + general prose and\n  contains almost no code/tech** (measured: ~48% news, ~51% prose, **15 of\n  ~11,000** documents technical). The technical quarter of the target is\n  therefore absent from training.\n- Injecting a technical-register quota changes the training mix's composition in\n  a directly measurable way (tech token share 0.2% → ~25–35%), and this is the\n  step that moves dev perplexity.\n\nPrediction: register composition of the selected head, not raw average\nclassifier score, tracks perplexity. Concretely, at fixed classifier quality,\nraising the technical share from ~0% toward ~25% **lowers** dev perplexity, and\nthe gain saturates once the register is adequately represented (diminishing\nreturns beyond ~25–35%).\n\nThis was borne out on the dev target (frozen `train_nano.py`):\nrandom 474.9 → classifier-only 412.3 → +English gate 410.8 →\n+25% tech quota 407.5 → +35% tech quota 404.6. The classifier captures the large\nfirst-order gain (quality/domain match); the register quota captures a real\nsecond-order gain (coverage) that pure ranking leaves on the table, and it\nsaturates — exactly the predicted shape.\n\n## Falsification\nThe claim is false if either holds:\n1. **Balance doesn't help.** If, holding the classifier fixed, adding a\n   technical-register quota (0% → 25%) does *not* reduce held-out perplexity (or\n   increases it), then coverage is not a distinct lever beyond similarity.\n2. **Similarity doesn't help.** If the classifier-ranked selection scores no\n   better than a random selection at equal token budget, the target-likeness\n   signal is worthless. (Observed: 412 vs 475 — clearly rejected.)\nA further falsifier: if perplexity kept dropping monotonically as tech share → 1,\nthe \"equal-parts coverage\" story would be wrong and it would just be \"more code\nis better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.\n\n## Transfer\nThe recipe transfers to any fixed-budget pretraining-data curation against a\n*known heterogeneous* target: (1) train a cheap linear classifier target-vs-pool\non hashed n-grams for a length-robust quality/domain score; (2) explicitly\ndetect each target register and enforce per-register token quotas so no\ncomponent of a multi-domain target is starved. It requires only a sample of the\ntarget distribution and needs no labels on the pool. The specific technical-\nsignal heuristic is domain-specific, but the two-part principle — *rank for\nquality, allocate for coverage* — is general, and is exactly what single-score\nselection methods (DSIR, perplexity filtering, one classifier) miss on\nmulti-domain targets.\n", "structuredPatch": [{"oldStart": 33, "oldLines": 7, "newStart": 33, "newLines": 10, "lines": [" +25% tech quota 407.5 → +35% tech quota 404.6. The classifier captures the large", " first-order gain (quality/domain match); the register quota captures a real", " second-order gain (coverage) that pure ranking leaves on the table, and it", "-saturates — exactly the predicted shape.", "+saturates — exactly the predicted shape. A NEWS-register *cap* (holding tech at", "+25%) was also tested and **regressed** dev perplexity (406.8 → 415.1),", "+confirming the mechanism is \"fill starved registers,\" not \"equalize all four", "+mechanically\": news-register prose is genuinely valuable for this target.", " ", " ## Falsification", " The claim is false if either holds:"]}], "userModified": false, "replaceAll": false}

The file /workspace/claim.md has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/claim.md", "oldString": "is better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.", "newString": "is better.\" Observed saturation (407.5 → 404.6 from 25%→35%) rejects that.", "originalFile": "# Curation claim\n\n## Hypothesis\nFor a fixed 12M-token budget, held-out perplexity on a *broad, equal-parts*\nmulti-domain target (Wikipedia / high-quality web prose / news / technical Q&A)\nis minimized not merely by selecting the documents most similar to the target,\nbut by selecting target-like documents **while enforcing coverage of every\ntarget register** — in particular the technical Q&A / code register, which a\nnaive similarity ranking starves. Both quality *and* register balance matter.\n\n## Mechanism (observable other than final perplexity)\nA logistic-regression classifier (target vs. random pool, on L2-normalized\nhashed unigram+bigram features) ranks documents by \"target-likeness.\" The\n**observable prediction** is a *compositional* one, checkable without training:\n\n- When the top-N documents that fill the 12M budget are bucketed by register,\n  a pure top-by-score selection is **dominated by news + general prose and\n  contains almost no code/tech** (measured: ~48% news, ~51% prose, **15 of\n  ~11,000** documents technical). The technical quarter of the target is\n  therefore absent from training.\n- Injecting a technical-register quota changes the training mix's composition in\n  a directly measurable way (tech token share 0.2% → ~25–35%), and this is the\n  step that moves dev perplexity.\n\nPrediction: register composition of the selected head, not raw average\nclassifier score, tracks perplexity. Concretely, at fixed classifier quality,\nraising the technical share from ~0% toward ~25% **lowers** dev perplexity, and\nthe gain saturates once the register is adequately represented (diminishing\nreturns beyond ~25–35%).\n\nThis was borne out on the dev target (frozen `train_nano.py`):\nrandom 474.9 → classifier-only 412.3 → +English gate 410.8 →\n+25% tech quota 407.5 → +35% tech quota 404.6. The classifier captures the large\nfirst-order gain (quality/domain match); the register quota captures a real\nsecond-order gain (coverage) that pure ranking leaves on the table, and it\nsaturates — exactly the predicted shape. A NEWS-register *cap* (holding tech at\n25%) was also tested and **regressed** dev perplexity (406.8 → 415.1),\nconfirming the mechanism is \"fill starved registers,\" not \"equalize all four\nmechanically\": news-register prose is genuinely valuable for this target.\n\n## Falsification\nThe claim is false if either holds:\n1. **Balance doesn't help.** If, holding the classifier fixed, adding a\n   technical-register quota (0% → 25%) does *not* reduce held-out perplexity (or\n   increases it), then coverage is not a distinct lever beyond similarity.\n2. **Similarity doesn't help.** If the classifier-ranked selection scores no\n   better than a random selection at equal token budget, the target-likeness\n   signal is worthless. (Observed: 412 vs 475 — clearly rejected.)\nA further falsifier: if perplexity kept dropping monotonically as tech share → 1,\nthe \"equal-parts coverage\" story would be wrong and it would just be \"more code\nis better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.\n\n## Transfer\nThe recipe transfers to any fixed-budget pretraining-data curation against a\n*known heterogeneous* target: (1) train a cheap linear classifier target-vs-pool\non hashed n-grams for a length-robust quality/domain score; (2) explicitly\ndetect each target register and enforce per-register token quotas so no\ncomponent of a multi-domain target is starved. It requires only a sample of the\ntarget distribution and needs no labels on the pool. The specific technical-\nsignal heuristic is domain-specific, but the two-part principle — *rank for\nquality, allocate for coverage* — is general, and is exactly what single-score\nselection methods (DSIR, perplexity filtering, one classifier) miss on\nmulti-domain targets.\n", "structuredPatch": [{"oldStart": 48, "oldLines": 7, "newStart": 48, "newLines": 7, "lines": ["    signal is worthless. (Observed: 412 vs 475 — clearly rejected.)", " A further falsifier: if perplexity kept dropping monotonically as tech share → 1,", " the \"equal-parts coverage\" story would be wrong and it would just be \"more code", "-is better.\" Observed saturation (407.5 → 406.8 from 25%→35%) rejects that.", "+is better.\" Observed saturation (407.5 → 404.6 from 25%→35%) rejects that.", " ", " ## Transfer", " The recipe transfers to any fixed-budget pretraining-data curation against a"]}], "userModified": false, "replaceAll": false}

packed 12000000 tokens from 27817 ids -> /tmp/dev_train.npy

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packed 12000000 tokens from 27817 ids -> /tmp/dev_train.npy
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packed 12000000 tokens from 27817 ids -> /tmp/dev_train.npy
HELDOUT_PPL 404.594  loss 6.0029  train_s 56  /tmp/dev_train.npy
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packed 12000000 tokens from 27817 ids -> /tmp/dev_train.npy
HELDOUT_PPL 404.594  loss 6.0029  train_s 56  /tmp/dev_train.npy
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=== deliverables ===
-rw-r--r-- 1 root root 3798 Aug  4 05:10 claim.md

submission/:
total 201
drwxr-xr-x 1 root root    100 Aug  4 05:05 .
drwxr-xr-x 1 root root    680 Aug  4 05:10 ..
-rw-r--r-- 1 root root   8004 Aug  4 05:06 curate.py
-rw-r--r-- 1 root root 197529 Aug  4 05:09 selection.json

selection.json: 27817 ids | unique: True | in-range: True

curate.py at both paths identical:
OK
time left:
1444

[stdout]
=== deliverables ===
-rw-r--r-- 1 root root 3798 Aug  4 05:10 claim.md

submission/:
total 201
drwxr-xr-x 1 root root    100 Aug  4 05:05 .
drwxr-xr-x 1 root root    680 Aug  4 05:10 ..
-rw-r--r-- 1 root root   8004 Aug  4 05:06 curate.py
-rw-r--r-- 1 root root 197529 Aug  4 05:09 selection.json

selection.json: 27817 ids | unique: True | in-range: True

curate.py at both paths identical:
OK
time left:
1444
[metadata] {"noOutputExpected": false}