claude-opus-5

Token indices sequence length is longer than the specified maximum sequence length for this model (1873 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3295 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (4186 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1121 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (9222 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3976 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (10486 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2410 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1768 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (7687 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (4180 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2367 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1727 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1933 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3724 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2957 > 1024). Running this sequence through the model will result in indexing errors
docs actually consumed to reach 12M: 13401
prefix length (ov=1.0) was 12983 -> reserve used: 418 docs
true chars/token = 4.248 (script assumes 4.05)
1245

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (1873 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3295 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (4186 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1121 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (9222 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3976 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (10486 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2410 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1768 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (7687 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (4180 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2367 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1727 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1933 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3724 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2957 > 1024). Running this sequence through the model will result in indexing errors
docs actually consumed to reach 12M: 13401
prefix length (ov=1.0) was 12983 -> reserve used: 418 docs
true chars/token = 4.248 (script assumes 4.05)
1245
[metadata] {"noOutputExpected": false}

Command running in background with ID: b1n78nk5y. Output is being written to: /tmp/claude-0/-workspace/5c34ea41-584d-44c9-bb08-1f0e72305853/tasks/b1n78nk5y.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": "b1n78nk5y", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

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": "**Noise floor.** Re-running the classifier changes weights in the last bits (GPU\nsparse-matmul accumulation order), shifting the selection by ~1 document in 26k and\nthe score by **0.45 ppl** (333.30 → 333.75). All differences cited above are well\nabove that floor; the 310.7 vs 311.4 difference at the flat end is not.", "newString": "**Noise floor — measured, and larger than it first appeared.** Training itself is\nbit-deterministic: the same token array trained twice gives *identical* ppl to three\ndecimals (315.557 / 315.557 and 304.252 / 304.252). All variance therefore comes\nfrom the selection. GPU sparse-matmul accumulation order is not bit-reproducible, so\nre-running the classifier perturbs near-tied scores. Two cold runs of the *final*\nscript produced the **identical set** of 37,407 ids in a slightly different order,\nand scored **315.6 vs 304.3 — an 11 ppl spread**.\n\nThis is the most important caveat in this write-up. An early estimate from a\nsingle-document perturbation (0.45 ppl) badly understated it. With a ±6 ppl noise\nband:\n\n* The **direction** of the prefix-depth result (M5) is real: the sweep spans\n  310.7 → 333.7, several times the noise band, and is monotone.\n* The **fine ranking** inside that sweep is not. 1.0 / 1.2 / 1.32 / 1.45\n  (310.7 / 311.4 / 315.3 / 314.3) are mutually indistinguishable, and the\n  chosen operating point should be read as \"shallow\", not as \"exactly 1.0\".\n* M4 (444.4 vs 333.3) and the headline result (477.8 vs ~310) are far outside it.\n\n**Why the jitter has this much leverage, and the fix.** The priority prefix is sized\nin *estimated* tokens (chars / 4.05) while the true ratio for these documents is\n**4.248 chars/token**, so the prefix under-delivers by ~5%: 12,983 prefix documents\nsupply only ~11.4M tokens and the trainer pulls the last **418** documents (3%) out\nof the reserve tail, where scores are near-tied and jitter reorders freely. Sizing\nthe prefix so it lands exactly on the budget makes the consumed set equal to the\nprefix set, which removes the reserve's contribution to the variance.", "originalFile": "# Curating a raw web pool for broad multi-domain perplexity\n\n**Result.** Random-selection baseline **477.8** dev ppl → curated selection **310.7**\ndev ppl, a **35% reduction**, under the identical frozen 12M-token recipe.\n\n---\n\n## Hypothesis\n\nThe 12M-token budget is ~6% of the pool, so the binding question is not \"which\ndocuments are good prose\" but \"which documents are drawn from the *same\ndistribution as the evaluation target*\". I claim:\n\n1. **A target-vs-pool discriminator is a sufficient quality signal.** The dev\n   target itself (`data/multi_dev.npy`) is the only ground truth available, and it\n   is enough: a cheap hashed-n-gram classifier trained to separate decoded target\n   documents from a random pool sample ranks pool documents well enough to beat\n   random selection by a wide margin. No external quality labels are needed.\n\n2. **The target is a mixture, so the selection must be a mixture.** The target is\n   four registers in equal quarters. A single pooled \"target-likeness\" score\n   collapses onto whichever register is easiest to match and leaves the others\n   badly fit; per-register selection with an explicit allocation rule does not.\n\n3. **The right allocation unit is documents, not tokens.** Matching the target's\n   *token* proportions is worse than allocating an equal *number of documents* per\n   register, because register document lengths differ ~5× and equal-doc-count\n   buys both topical diversity (many short encyclopedic/Q&A documents) and\n   long-range well-edited context (fewer, longer news documents).\n\n## Mechanism — predictions other than the final perplexity\n\nThese are the observables the mechanism commits to; all were measured *during*\ndevelopment, and the numbers below are the measurements.\n\n* **M1. The discriminator must generalise, not memorise.** If target-likeness is a\n  real learnable property, a classifier trained on ~80% of the decoded target\n  documents should classify *held-out* target documents into the correct register\n  far above chance. → **0.867 vs 0.20 chance** (5-way, held-out).\n\n* **M2. Loss should be unevenly distributed across registers, and the encyclopedic\n  register should be the worst.** WikiText-style text carries detokenisation\n  artifacts (` ,`, ` .`, ` @,@ `, ` @-@ `) absent from raw web text, so those tokens\n  are near-unpredictable regardless of content match. → per-register dev ppl\n  **849 / 268 / 336 / 161** (wiki / news / web-prose / tech-QA). The encyclopedic\n  register is 3–5× worse than the rest, and **12.7% of its tokens** are\n  artifact-form tokens versus 0.4% in news and web prose.\n\n* **M3. That gap is a surface-form mismatch, not a content mismatch — so it should\n  be *irreducible by selection*.** Prediction: the pool contains no clean\n  spaced-punctuation prose to fix it with. → confirmed. 0 pool documents contain\n  ` @,@ `/` @-@ `; the 1,658 documents with >15% spaced punctuation are SEO\n  keyword-spam, wiring-diagram link farms and non-English (inspected directly), and\n  the spacing comes from comma-separated keyword lists rather than prose. Selecting\n  them would trade a small punctuation gain for a large prose-quality loss.\n  Consequently, reweighting the mixture toward the encyclopedic register should\n  *not* help: → it does not (0.40 wiki weight = **346.6**, worse than baseline mix).\n\n* **M4. Balanced beats pooled.** Same scores, same filters, only the allocation\n  rule differs. → pooled top-k **444.4** vs per-register **333.3**. Pooled selection\n  recovers only 23% of the gap from random, per-register recovers 46% at that stage.\n\n* **M5. Strictness dominates.** Because only the top of each ranked list is worth\n  spending budget on, shrinking the priority prefix toward exactly-the-budget should\n  monotonically improve the score. → prefix depth sweep (× budget):\n  **1.0 → 310.7**, 1.2 → 311.4, 1.32 → 315.3, 1.45 → 314.3, 1.6 → 324.2,\n  1.8 → 328.4, 2.2 → 333.7. Monotone, flattening at the shallow end.\n\n* **M6. The mix is not the mechanism.** Explicitly reproducing the winning run's\n  realised token mix (7/69/18/6) while choosing documents by token-quota instead of\n  doc-count should *fail* to reproduce its score. → **353.1** vs 333.3. So the gain\n  comes from *which documents* are selected, not from the ratio they happen to\n  produce. (This corrected an earlier misreading of mine: the 333 run's \"69% news\"\n  was an artifact of attributing each document to its argmax register rather than to\n  the register whose list actually selected it.)\n\n**Noise floor.** Re-running the classifier changes weights in the last bits (GPU\nsparse-matmul accumulation order), shifting the selection by ~1 document in 26k and\nthe score by **0.45 ppl** (333.30 → 333.75). All differences cited above are well\nabove that floor; the 310.7 vs 311.4 difference at the flat end is not.\n\n## Falsification\n\nThe claim is wrong if any of these hold:\n\n* A **pooled** (non-per-register) ranking on the same scores matches per-register\n  allocation. Measured: it does not (444.4 vs 333.3, M4).\n* Equal-**token**-share allocation beats equal-**document**-count. Measured: it does\n  not (339.3 / 340.1 / 346.6 across three weightings vs 310.7–333.7, M3/M6).\n* The hidden held-out ppl lands near the 477.8 random baseline rather than near\n  310.7. That would mean the discriminator latched onto sample-specific\n  idiosyncrasies of `multi_dev.npy` rather than the register distributions. The\n  guard against this is that the classifier's positives are only ~2k short documents\n  while the features are 2^17 hashed buckets with L2 penalty and a held-out-selected\n  checkpoint — and that no per-register weight was tuned to dev beyond confirming\n  equal-ish allocation.\n* **Residual risk I did not eliminate:** the prefix-depth knob (M5) was tuned on\n  `multi_dev.npy` over 7 runs. Its flat optimum (1.0–1.2 within noise) and monotone\n  shape argue it is a genuine strictness effect rather than dev-set fitting, but a\n  single hidden-set run cannot distinguish these, and I chose the dev-best endpoint.\n\n## Transfer\n\n* **The dev/validation target is a legitimate and very strong selection signal.**\n  Whenever a held-out target sample is disclosed, decoding it into positives and\n  training a cheap n-gram discriminator against the raw pool is a ~60-second\n  operation that here bought a 35% perplexity reduction — far more than any\n  hand-written quality heuristic in the same pipeline.\n* **Select per component of a mixture target, and allocate by document count.**\n  For any multi-domain objective, rank within each component separately and\n  interleave. Allocating by token share silently lets the long-document component\n  dominate; allocating by document count is both simpler and, here, better.\n* **Diagnose before optimising.** The per-register loss breakdown (M2) plus the\n  artifact-token count (M3) took ~5 minutes and showed that the single largest loss\n  term was *unfixable by selection*, which redirected effort to the strictness knob\n  that actually paid (M5). Without it, the obvious move — pour budget into the\n  worst register — was available, and it loses (346.6).\n* **At a fixed small budget, be strict.** The budget is better spent on fewer,\n  higher-ranked documents than on a broader sweep of merely-acceptable ones; the\n  monotone prefix-depth sweep is the cleanest single result here.\n\n## Reproduction\n\n```bash\npython /workspace/submission/curate.py      # defaults: --mode docs --overfill 1.0 --reserve 2.5\nbash  /workspace/dev_eval.sh /workspace/submission/selection.json\n```\n\n`curate.py` writes 37,407 ids in priority order; the frozen pipeline consumes\n18,365 of them to reach exactly 12,000,000 GPT-2 tokens, leaving the rest as spare\ncapacity so the budget cannot underfill.\n", "structuredPatch": [{"oldStart": 74, "oldLines": 11, "newStart": 74, "newLines": 33, "lines": ["   was an artifact of attributing each document to its argmax register rather than to", "   the register whose list actually selected it.)", " ", "-**Noise floor.** Re-running the classifier changes weights in the last bits (GPU", "-sparse-matmul accumulation order), shifting the selection by ~1 document in 26k and", "-the score by **0.45 ppl** (333.30 → 333.75). All differences cited above are well", "-above that floor; the 310.7 vs 311.4 difference at the flat end is not.", "+**Noise floor — measured, and larger than it first appeared.** Training itself is", "+bit-deterministic: the same token array trained twice gives *identical* ppl to three", "+decimals (315.557 / 315.557 and 304.252 / 304.252). All variance therefore comes", "+from the selection. GPU sparse-matmul accumulation order is not bit-reproducible, so", "+re-running the classifier perturbs near-tied scores. Two cold runs of the *final*", "+script produced the **identical set** of 37,407 ids in a slightly different order,", "+and scored **315.6 vs 304.3 — an 11 ppl spread**.", " ", "+This is the most important caveat in this write-up. An early estimate from a", "+single-document perturbation (0.45 ppl) badly understated it. With a ±6 ppl noise", "+band:", "+", "+* The **direction** of the prefix-depth result (M5) is real: the sweep spans", "+  310.7 → 333.7, several times the noise band, and is monotone.", "+* The **fine ranking** inside that sweep is not. 1.0 / 1.2 / 1.32 / 1.45", "+  (310.7 / 311.4 / 315.3 / 314.3) are mutually indistinguishable, and the", "+  chosen operating point should be read as \"shallow\", not as \"exactly 1.0\".", "+* M4 (444.4 vs 333.3) and the headline result (477.8 vs ~310) are far outside it.", "+", "+**Why the jitter has this much leverage, and the fix.** The priority prefix is sized", "+in *estimated* tokens (chars / 4.05) while the true ratio for these documents is", "+**4.248 chars/token**, so the prefix under-delivers by ~5%: 12,983 prefix documents", "+supply only ~11.4M tokens and the trainer pulls the last **418** documents (3%) out", "+of the reserve tail, where scores are near-tied and jitter reorders freely. Sizing", "+the prefix so it lands exactly on the budget makes the consumed set equal to the", "+prefix set, which removes the reserve's contribution to the variance.", "+", " ## Falsification", " ", " The claim is wrong if any of these hold:"]}], "userModified": false, "replaceAll": false}

[02:06:25] prefix 12370 ids + reserve tail 24532 ids (~30.0M est spare tokens)
packed 12000000 tokens from 36902 ids (18218 tokenized) -> /tmp/tr_cal.npy
HELDOUT_PPL 302.369  loss 5.7116  train_s 56  /tmp/tr_cal.npy
1095

[stdout]
[02:06:25] prefix 12370 ids + reserve tail 24532 ids (~30.0M est spare tokens)
packed 12000000 tokens from 36902 ids (18218 tokenized) -> /tmp/tr_cal.npy
HELDOUT_PPL 302.369  loss 5.7116  train_s 56  /tmp/tr_cal.npy
1095
[metadata] {"noOutputExpected": false}

192:    ap.add_argument("--overfill", type=float, default=0.953,
n ids        : 36902
all ints     : True
no duplicates: True
all in pool  : True

[stdout]
192:    ap.add_argument("--overfill", type=float, default=0.953,
n ids        : 36902
all ints     : True
no duplicates: True
all in pool  : True
[metadata] {"noOutputExpected": false}

ok
144
packed 12000000 tokens from 36902 ids -> /tmp/off2.npy
1005

[stdout]
ok
144
packed 12000000 tokens from 36902 ids -> /tmp/off2.npy
1005
[metadata] {"noOutputExpected": false}

official array == evaluated array: True 12000000
claim.md updated
=== deliverables ===
-rw-r--r-- 1 root root  10277 Aug  4 02:09 claim.md
-rw-r--r-- 1 root root  21472 Aug  4 02:08 submission/curate.py
-rw-r--r-- 1 root root 257888 Aug  4 02:08 submission/selection.json
selection.json entries: 36902
curate.py parses OK
995

[stdout]
official array == evaluated array: True 12000000
claim.md updated
=== deliverables ===
-rw-r--r-- 1 root root  10277 Aug  4 02:09 claim.md
-rw-r--r-- 1 root root  21472 Aug  4 02:08 submission/curate.py
-rw-r--r-- 1 root root 257888 Aug  4 02:08 submission/selection.json
selection.json entries: 36902
curate.py parses OK
995
[metadata] {"noOutputExpected": false}