claude-opus-4-8

Command running in background with ID: bagc3sgck. Output is being written to: /tmp/claude-0/-workspace/010b8dbe-8f98-478c-a3eb-e2fc1f0f67e0/tasks/bagc3sgck.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": "bagc3sgck", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

OUT  = "/tmp/sel_wiki.json"
PROP          = {"wiki": 0.24, "qa": 0.25, "newsweb": 0.51}  # target token mixture
---
curate wiki log:
time left: 2757s

[stdout]
OUT  = "/tmp/sel_wiki.json"
PROP          = {"wiki": 0.24, "qa": 0.25, "newsweb": 0.51}  # target token mixture
---
curate wiki log:
time left: 2757s
[metadata] {"noOutputExpected": false}

Updated task #3 status

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

Updated task #2 status

[metadata] {"success": true, "taskId": "2", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "in_progress"}}

curate:
[fmt] front-loaded 271 HTML-format docs into qa stream
[out] 94642 ids, ~40.0M est tokens, mix={'wiki': 0.24, 'qa': 0.25, 'newsweb': 0.51} -> /tmp/sel_wiki.json
eval:
time left: 2627s

[stdout]
curate:
[fmt] front-loaded 271 HTML-format docs into qa stream
[out] 94642 ids, ~40.0M est tokens, mix={'wiki': 0.24, 'qa': 0.25, 'newsweb': 0.51} -> /tmp/sel_wiki.json
eval:
time left: 2627s
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 305.199  loss 5.7210  train_s 53  /tmp/dev_train.npy
DONE_WIKI
--- vs v4 baseline: 300.49 ---
time left: 2547s

[stdout]
HELDOUT_PPL 305.199  loss 5.7210  train_s 53  /tmp/dev_train.npy
DONE_WIKI
--- vs v4 baseline: 300.49 ---
time left: 2547s
[metadata] {"noOutputExpected": false}

submission/selection.json STILL = v4 (300.49), untouched
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[stdout]
submission/selection.json STILL = v4 (300.49), untouched
time left: 2537s
[metadata] {"noOutputExpected": 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": "- **Ablation:** removing the format front-loading (register-match only) raises\n  overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A register,\n  not a uniform shift. (Two runs, identical otherwise.)", "newString": "- **Format ablation:** removing the format front-loading (register-match only)\n  raises overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A\n  register, not a uniform shift. (Two runs, identical otherwise.)\n- **Mixture ablation:** deviating from the matched mixture toward wiki\n  (24/25/51 instead of 16/25/59) *raises* dev ppl **300.5 → 305.2**, confirming\n  wiki's high loss is intrinsic (artifact floor + encyclopedic hardness), not\n  under-training — so matching the target token mixture is at/near the optimum.", "originalFile": "# Curation claim\n\n## Hypothesis\nUnder a fixed 12M-token budget (~6% of the pool), held-out perplexity on the\nbroad multi-domain target is minimized by making the **training token\ndistribution match the target's own distribution** — both its *register mixture*\nand its *surface forms* — rather than by maximizing any single notion of\n\"quality.\" Concretely, selecting pool documents with per-register domain\nclassifiers and composing them to reproduce the target's measured token mixture\n(**wiki ≈16% / technical-Q&A ≈25% / news+web-prose ≈59%**), plus front-loading\nthe pool's rare HTML-formatted documents into the Q&A stream, should beat a random\nselection by a wide margin.\n\nMeasured (dev ppl, identical frozen trainer, data-selection only):\n\n| selection                                   | dev ppl | mean loss |\n|---------------------------------------------|--------:|----------:|\n| random baseline (do-nothing)                |  457.99 |    6.127  |\n| register-match only                         |  355.66 |    5.874  |\n| + front-load HTML-format docs into qa       |  312.88 |    5.746  |\n| + Latin/clean + repetition + near-dedup     | **300.49** | **5.705** |\n\nA **34.4%** perplexity reduction vs. random, produced entirely by which\ndocuments are selected.\n\n## Mechanism (and a prediction about an observable *other* than final perplexity)\nHeld-out cross-entropy is a *per-token* average, so it decomposes by register:\n`loss = Σ_r f_r · loss_r`, with `f_r` the target's token fraction in register `r`.\nTwo forces set `loss_r`:\n1. **Register/domain match** — a register is modeled well only if the 12M-token\n   training mix contains enough in-domain tokens; matching `f_r` is the mixture\n   that minimizes the weighted sum given the pool.\n2. **Surface-form coverage** — tokens that are frequent in the target but nearly\n   absent from the pool (the WikiText `@-@ / @,@ / @.@` artifacts; the\n   StackOverflow `<p> … </p>`, `<pre><code>` tags) carry near-maximal loss unless\n   the training set exposes them.\n\n**Predicted observables (not the final ppl), all confirmed by a per-register\ndiagnostic eval of the trained model:**\n- The three registers are **ordered qa < newsweb < wiki** in held-out loss —\n  qa the *lowest* (160 ppl) *because* the ~271 rare `<p>/<code>/<pre>/<br>` pool\n  docs were front-loaded, giving the model the exact format tokens; wiki the\n  *highest* (758 ppl). Observed per-register (final selection): qa 5.078,\n  newsweb 5.713, wiki 6.631 nats.\n- **Ablation:** removing the format front-loading (register-match only) raises\n  overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A register,\n  not a uniform shift. (Two runs, identical otherwise.)\n- **Irreducible wiki floor:** the `@`-artifact tokens are **4.6%** of wiki eval\n  tokens and appear in **0 / 182,016** pool documents, so wiki loss cannot be\n  driven below a fixed floor by *any* selection — it is not where curation can\n  win, and it penalizes the random baseline equally. This is *why* the marginal\n  budget is best spent on news/web (59% of eval, `loss_r`≈average) rather than on\n  wiki: with eval weights fixed, every register-budget shift I computed is\n  neutral-to-worse, so matching the target mixture is near-optimal.\n\n## Falsification\nThe mechanism is wrong if any of the following holds:\n- A selection that **ignores the register mixture** (e.g., pure top-classifier\n  score, which skews heavily to one register) reaches **equal or lower** held-out\n  perplexity than the mixture-matched selection.\n- Front-loading the HTML-format docs does **not** lower the Q&A register's\n  held-out loss specifically (i.e., the 312.9↔355.7 gap disappears or shows up\n  uniformly across registers).\n- Pool documents **do** contain the `@-@` artifacts, or removing them from the\n  wiki-eval accounting does **not** collapse most of the wiki–qa loss gap —\n  which would mean the wiki floor is content, not surface form.\n\n## Transfer\nThe recipe transfers to any fixed-budget pretraining-curation problem with (i) a\nlabeled sample of the eval target and (ii) a broad, heterogeneous pool: measure\nthe target's token-domain mixture, train one lightweight classifier per domain\n(target-domain vs. random pool) to rank the pool, select by **token-weighted\nround-robin** to reproduce that mixture, and explicitly guarantee coverage of\nrare-but-target-frequent surface forms. It transfers to the **hidden official\ntarget** because that target is a disjoint sample from the *same construction*\n(same registers, same WikiText/StackOverflow formats), so register-matching and\nformat-exposure generalize beyond the specific dev documents (the classifiers key\non content vocabulary, not on memorized dev strings). It would **not** transfer if\nthe official target's domain mixture or formats differed materially from the dev\nsample's — the one assumption this method depends on.\n", "structuredPatch": [{"oldStart": 42, "oldLines": 9, "newStart": 42, "newLines": 13, "lines": ["   docs were front-loaded, giving the model the exact format tokens; wiki the", "   *highest* (758 ppl). Observed per-register (final selection): qa 5.078,", "   newsweb 5.713, wiki 6.631 nats.", "-- **Ablation:** removing the format front-loading (register-match only) raises", "-  overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A register,", "-  not a uniform shift. (Two runs, identical otherwise.)", "+- **Format ablation:** removing the format front-loading (register-match only)", "+  raises overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A", "+  register, not a uniform shift. (Two runs, identical otherwise.)", "+- **Mixture ablation:** deviating from the matched mixture toward wiki", "+  (24/25/51 instead of 16/25/59) *raises* dev ppl **300.5 → 305.2**, confirming", "+  wiki's high loss is intrinsic (artifact floor + encyclopedic hardness), not", "+  under-training — so matching the target token mixture is at/near the optimum.", " - **Irreducible wiki floor:** the `@`-artifact tokens are **4.6%** of wiki eval", "   tokens and appear in **0 / 182,016** pool documents, so wiki loss cannot be", "   driven below a fixed floor by *any* selection — it is not where curation can"]}], "userModified": false, "replaceAll": false}

1	# Curation claim
2	
3	## Hypothesis
4	Under a fixed 12M-token budget (~6% of the pool), held-out perplexity on the
5	broad multi-domain target is minimized by making the **training token
6	distribution match the target's own distribution** — both its *register mixture*
7	and its *surface forms* — rather than by maximizing any single notion of
8	"quality." Concretely, selecting pool documents with per-register domain
9	classifiers and composing them to reproduce the target's measured token mixture
10	(**wiki ≈16% / technical-Q&A ≈25% / news+web-prose ≈59%**), plus front-loading
11	the pool's rare HTML-formatted documents into the Q&A stream, should beat a random
12	selection by a wide margin.
13	
14	Measured (dev ppl, identical frozen trainer, data-selection only):
15	
16	| selection                                   | dev ppl | mean loss |
17	|---------------------------------------------|--------:|----------:|
18	| random baseline (do-nothing)                |  457.99 |    6.127  |
19	| register-match only                         |  355.66 |    5.874  |
20	| + front-load HTML-format docs into qa       |  312.88 |    5.746  |
21	| + Latin/clean + repetition + near-dedup     | **300.49** | **5.705** |
22	
23	A **34.4%** perplexity reduction vs. random, produced entirely by which
24	documents are selected.
25	
26	## Mechanism (and a prediction about an observable *other* than final perplexity)
27	Held-out cross-entropy is a *per-token* average, so it decomposes by register:
28	`loss = Σ_r f_r · loss_r`, with `f_r` the target's token fraction in register `r`.
29	Two forces set `loss_r`:
30	1. **Register/domain match** — a register is modeled well only if the 12M-token
31	   training mix contains enough in-domain tokens; matching `f_r` is the mixture
32	   that minimizes the weighted sum given the pool.
33	2. **Surface-form coverage** — tokens that are frequent in the target but nearly
34	   absent from the pool (the WikiText `@-@ / @,@ / @.@` artifacts; the
35	   StackOverflow `<p> … </p>`, `<pre><code>` tags) carry near-maximal loss unless
36	   the training set exposes them.
37	
38	**Predicted observables (not the final ppl), all confirmed by a per-register
39	diagnostic eval of the trained model:**
40	- The three registers are **ordered qa < newsweb < wiki** in held-out loss —
41	  qa the *lowest* (160 ppl) *because* the ~271 rare `<p>/<code>/<pre>/<br>` pool
42	  docs were front-loaded, giving the model the exact format tokens; wiki the
43	  *highest* (758 ppl). Observed per-register (final selection): qa 5.078,
44	  newsweb 5.713, wiki 6.631 nats.
45	- **Format ablation:** removing the format front-loading (register-match only)
46	  raises overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A
47	  register, not a uniform shift. (Two runs, identical otherwise.)
48	- **Mixture ablation:** deviating from the matched mixture toward wiki
49	  (24/25/51 instead of 16/25/59) *raises* dev ppl **300.5 → 305.2**, confirming
50	  wiki's high loss is intrinsic (artifact floor + encyclopedic hardness), not
51	  under-training — so matching the target token mixture is at/near the optimum.
52	- **Irreducible wiki floor:** the `@`-artifact tokens are **4.6%** of wiki eval
53	  tokens and appear in **0 / 182,016** pool documents, so wiki loss cannot be
54	  driven below a fixed floor by *any* selection — it is not where curation can
55	  win, and it penalizes the random baseline equally. This is *why* the marginal
56	  budget is best spent on news/web (59% of eval, `loss_r`≈average) rather than on
57	  wiki: with eval weights fixed, every register-budget shift I computed is
58	  neutral-to-worse, so matching the target mixture is near-optimal.
59	
60	## Falsification
61	The mechanism is wrong if any of the following holds:
62	- A selection that **ignores the register mixture** (e.g., pure top-classifier
63	  score, which skews heavily to one register) reaches **equal or lower** held-out
64	  perplexity than the mixture-matched selection.
65	- Front-loading the HTML-format docs does **not** lower the Q&A register's
66	  held-out loss specifically (i.e., the 312.9↔355.7 gap disappears or shows up
67	  uniformly across registers).
68	- Pool documents **do** contain the `@-@` artifacts, or removing them from the
69	  wiki-eval accounting does **not** collapse most of the wiki–qa loss gap —
70	  which would mean the wiki floor is content, not surface form.
71	
72	## Transfer
73	The recipe transfers to any fixed-budget pretraining-curation problem with (i) a
74	labeled sample of the eval target and (ii) a broad, heterogeneous pool: measure
75	the target's token-domain mixture, train one lightweight classifier per domain
76	(target-domain vs. random pool) to rank the pool, select by **token-weighted
77	round-robin** to reproduce that mixture, and explicitly guarantee coverage of
78	rare-but-target-frequent surface forms. It transfers to the **hidden official
79	target** because that target is a disjoint sample from the *same construction*
80	(same registers, same WikiText/StackOverflow formats), so register-matching and
81	format-exposure generalize beyond the specific dev documents (the classifiers key
82	on content vocabulary, not on memorized dev strings). It would **not** transfer if
83	the official target's domain mixture or formats differed materially from the dev
84	sample's — the one assumption this method depends on.
85

[metadata] {"type": "text", "file": {"filePath": "/workspace/claim.md", "content": "# Curation claim\n\n## Hypothesis\nUnder a fixed 12M-token budget (~6% of the pool), held-out perplexity on the\nbroad multi-domain target is minimized by making the **training token\ndistribution match the target's own distribution** — both its *register mixture*\nand its *surface forms* — rather than by maximizing any single notion of\n\"quality.\" Concretely, selecting pool documents with per-register domain\nclassifiers and composing them to reproduce the target's measured token mixture\n(**wiki ≈16% / technical-Q&A ≈25% / news+web-prose ≈59%**), plus front-loading\nthe pool's rare HTML-formatted documents into the Q&A stream, should beat a random\nselection by a wide margin.\n\nMeasured (dev ppl, identical frozen trainer, data-selection only):\n\n| selection                                   | dev ppl | mean loss |\n|---------------------------------------------|--------:|----------:|\n| random baseline (do-nothing)                |  457.99 |    6.127  |\n| register-match only                         |  355.66 |    5.874  |\n| + front-load HTML-format docs into qa       |  312.88 |    5.746  |\n| + Latin/clean + repetition + near-dedup     | **300.49** | **5.705** |\n\nA **34.4%** perplexity reduction vs. random, produced entirely by which\ndocuments are selected.\n\n## Mechanism (and a prediction about an observable *other* than final perplexity)\nHeld-out cross-entropy is a *per-token* average, so it decomposes by register:\n`loss = Σ_r f_r · loss_r`, with `f_r` the target's token fraction in register `r`.\nTwo forces set `loss_r`:\n1. **Register/domain match** — a register is modeled well only if the 12M-token\n   training mix contains enough in-domain tokens; matching `f_r` is the mixture\n   that minimizes the weighted sum given the pool.\n2. **Surface-form coverage** — tokens that are frequent in the target but nearly\n   absent from the pool (the WikiText `@-@ / @,@ / @.@` artifacts; the\n   StackOverflow `<p> … </p>`, `<pre><code>` tags) carry near-maximal loss unless\n   the training set exposes them.\n\n**Predicted observables (not the final ppl), all confirmed by a per-register\ndiagnostic eval of the trained model:**\n- The three registers are **ordered qa < newsweb < wiki** in held-out loss —\n  qa the *lowest* (160 ppl) *because* the ~271 rare `<p>/<code>/<pre>/<br>` pool\n  docs were front-loaded, giving the model the exact format tokens; wiki the\n  *highest* (758 ppl). Observed per-register (final selection): qa 5.078,\n  newsweb 5.713, wiki 6.631 nats.\n- **Format ablation:** removing the format front-loading (register-match only)\n  raises overall dev ppl **312.9 → 355.7** — an effect concentrated in the Q&A\n  register, not a uniform shift. (Two runs, identical otherwise.)\n- **Mixture ablation:** deviating from the matched mixture toward wiki\n  (24/25/51 instead of 16/25/59) *raises* dev ppl **300.5 → 305.2**, confirming\n  wiki's high loss is intrinsic (artifact floor + encyclopedic hardness), not\n  under-training — so matching the target token mixture is at/near the optimum.\n- **Irreducible wiki floor:** the `@`-artifact tokens are **4.6%** of wiki eval\n  tokens and appear in **0 / 182,016** pool documents, so wiki loss cannot be\n  driven below a fixed floor by *any* selection — it is not where curation can\n  win, and it penalizes the random baseline equally. This is *why* the marginal\n  budget is best spent on news/web (59% of eval, `loss_r`≈average) rather than on\n  wiki: with eval weights fixed, every register-budget shift I computed is\n  neutral-to-worse, so matching the target mixture is near-optimal.\n\n## Falsification\nThe mechanism is wrong if any of the following holds:\n- A selection that **ignores the register mixture** (e.g., pure top-classifier\n  score, which skews heavily to one register) reaches **equal or lower** held-out\n  perplexity than the mixture-matched selection.\n- Front-loading the HTML-format docs does **not** lower the Q&A register's\n  held-out loss specifically (i.e., the 312.9↔355.7 gap disappears or shows up\n  uniformly across registers).\n- Pool documents **do** contain the `@-@` artifacts, or removing them from the\n  wiki-eval accounting does **not** collapse most of the wiki–qa loss gap —\n  which would mean the wiki floor is content, not surface form.\n\n## Transfer\nThe recipe transfers to any fixed-budget pretraining-curation problem with (i) a\nlabeled sample of the eval target and (ii) a broad, heterogeneous pool: measure\nthe target's token-domain mixture, train one lightweight classifier per domain\n(target-domain vs. random pool) to rank the pool, select by **token-weighted\nround-robin** to reproduce that mixture, and explicitly guarantee coverage of\nrare-but-target-frequent surface forms. It transfers to the **hidden official\ntarget** because that target is a disjoint sample from the *same construction*\n(same registers, same WikiText/StackOverflow formats), so register-matching and\nformat-exposure generalize beyond the specific dev documents (the classifiers key\non content vocabulary, not on memorized dev strings). It would **not** transfer if\nthe official target's domain mixture or formats differed materially from the dev\nsample's — the one assumption this method depends on.\n", "numLines": 85, "startLine": 1, "totalLines": 85}}

=== curate.py PROP (should be matched 16/25/59) ===
PROP          = {"wiki": 0.16, "qa": 0.25, "newsweb": 0.59}  # target token mixture

=== final deliverables ===
submission/selection.json    675342 bytes
submission/curate.py         10707 bytes
claim.md                     5221 bytes

=== selection.json sanity ===
type list | n ids 93914 | unique True | all int True
priority head: [20313, 6138, 82269, 103647, 86993, 52768]

time left: 2497s

[stdout]
=== curate.py PROP (should be matched 16/25/59) ===
PROP          = {"wiki": 0.16, "qa": 0.25, "newsweb": 0.59}  # target token mixture

=== final deliverables ===
submission/selection.json    675342 bytes
submission/curate.py         10707 bytes
claim.md                     5221 bytes

=== selection.json sanity ===
type list | n ids 93914 | unique True | all int True
priority head: [20313, 6138, 82269, 103647, 86993, 52768]

time left: 2497s
[metadata] {"noOutputExpected": false}

Updated task #2 status

[metadata] {"success": true, "taskId": "2", "updatedFields": ["status"], "statusChange": {"from": "in_progress", "to": "completed"}}