claude-opus-4-8

pool: 182016 docs, 199911772 tokens, 0.2s
positives: 2449 dev pseudo-docs
classifier trained 11.1s
quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
emitted 36827 ids, 24000994 tokens across clusters; per-cluster tokens: [0.136 0.166 0.375 0.03  0.033 0.087 0.174 0.   ]
wrote /workspace/submission/selection.json  (20.1s)
4229

[stdout]
pool: 182016 docs, 199911772 tokens, 0.2s
positives: 2449 dev pseudo-docs
classifier trained 11.1s
quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
emitted 36827 ids, 24000994 tokens across clusters; per-cluster tokens: [0.136 0.166 0.375 0.03  0.033 0.087 0.174 0.   ]
wrote /workspace/submission/selection.json  (20.1s)
4229
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 36827 ids -> /tmp/bal_train.npy
HELDOUT_PPL 315.756  loss 5.7550  train_s 55  /tmp/bal_train.npy
4149

[stdout]
packed 12000000 tokens from 36827 ids -> /tmp/bal_train.npy
HELDOUT_PPL 315.756  loss 5.7550  train_s 55  /tmp/bal_train.npy
4149
[metadata] {"noOutputExpected": false}

=== cluster 2 (pool n=30391) ===
  *sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community: If you have set yourself on fire, do not run. Okay? Okay?? Please? Look, D, you had two months to say to Harvard in private emails, "Im sorry, I shouldnt have been
  News of the Week Barrie Spring Studio Tour April 27th & 28th 10:00 til 4:00 pm Come on down to Jill Price Studios this weekend to check out works I have created over the last year, as well as find some neat works from my artistic past in tje awesome sales bins created just for this weekend

=== cluster 5 (pool n=5506) ===
  Subject: Binary updates all installed pkgs? To: None <tech-pkg@NetBSD.org> From: Mark Weinem <firstname.lastname@example.org> List: tech-pkg Date: 02/21/2005 22:20:06 How to update all installed pkgs using binary packages? So
  The Lockout (is over!!!!!) View Single Post 12-05-2012, 04:34 PM Join Date: Mar 2011 Originally Posted by Bruce Arthur @bruce_arthur We were just all watching Ron Burkle talk with Don Fehr in a hallway. Other owners flitted around, too. Everyone seemed

=== cluster 6 (pool n=31581) ===
  Free the Cans! Working Together to Reduce Waste In a blog about how people share, it’s worth the occasional reference to the bizarre ways that people DON’T SHARE. Is it safe to say we live in a society that places great value on independence, private property, personal space, and privacy? Even sometimes extreme value
  ORLANDO, Fla. — While the Rapid Recall Exchange, the 2-year-old industry recall portal, has signed up more than 600 manufacturer and retailer subscribers, it still lacks the “critical mass” of suppliers that would make it a primary source of recall information, according to trade association officials and retailers. Manufacturers use the

=== cluster 0 (pool n=45690) ===
  Kraft Foods has taken the Cadbury chocolate brand in a new direction, by combining it with cheese for the first time. The company is bringing together two of its brands and launching Philadelphia with Cadbury, a chilled chocolate spread made from Philadelphia Light and Cadbury chocolate. Kraft believes the new product has the potential to do very well and
  You must be a registered member to view this page.| If you are already a member, sign in now. To register for your own account, sign up now. Signing up will REMOVE MOST OF THE ANNOYING ADS from your screen. CLICK HERE TO CREATE YOUR ACCOUNT - Get advice

=== cluster 1 (pool n=27746) ===
  Tallahassee, FL (Sports Network) - An appearance in the 2013 MEAC-SWAC Challenge, a trip to Ohio State and five home games highlight the Florida A&M football schedule announced on Thursday. The Rattlers, who have a new head coach in Earl Holmes, will play 12 games, including five at home for the
  - TV & Video - Renovation & Decorating - How To - Be Healthy - home beautiful You can also make one large shakshuka to share. Just spoon the sauce into a large ovenproof ceramic dish. Crack eggs into the dish, spaced evenly apart, then cook following the same recipe. 1.

[stdout]
=== cluster 2 (pool n=30391) ===
  *sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community: If you have set yourself on fire, do not run. Okay? Okay?? Please? Look, D, you had two months to say to Harvard in private emails, "Im sorry, I shouldnt have been
  News of the Week Barrie Spring Studio Tour April 27th & 28th 10:00 til 4:00 pm Come on down to Jill Price Studios this weekend to check out works I have created over the last year, as well as find some neat works from my artistic past in tje awesome sales bins created just for this weekend

=== cluster 5 (pool n=5506) ===
  Subject: Binary updates all installed pkgs? To: None <tech-pkg@NetBSD.org> From: Mark Weinem <firstname.lastname@example.org> List: tech-pkg Date: 02/21/2005 22:20:06 How to update all installed pkgs using binary packages? So
  The Lockout (is over!!!!!) View Single Post 12-05-2012, 04:34 PM Join Date: Mar 2011 Originally Posted by Bruce Arthur @bruce_arthur We were just all watching Ron Burkle talk with Don Fehr in a hallway. Other owners flitted around, too. Everyone seemed

=== cluster 6 (pool n=31581) ===
  Free the Cans! Working Together to Reduce Waste In a blog about how people share, it’s worth the occasional reference to the bizarre ways that people DON’T SHARE. Is it safe to say we live in a society that places great value on independence, private property, personal space, and privacy? Even sometimes extreme value
  ORLANDO, Fla. — While the Rapid Recall Exchange, the 2-year-old industry recall portal, has signed up more than 600 manufacturer and retailer subscribers, it still lacks the “critical mass” of suppliers that would make it a primary source of recall information, according to trade association officials and retailers. Manufacturers use the

=== cluster 0 (pool n=45690) ===
  Kraft Foods has taken the Cadbury chocolate brand in a new direction, by combining it with cheese for the first time. The company is bringing together two of its brands and launching Philadelphia with Cadbury, a chilled chocolate spread made from Philadelphia Light and Cadbury chocolate. Kraft believes the new product has the potential to do very well and
  You must be a registered member to view this page.| If you are already a member, sign in now. To register for your own account, sign up now. Signing up will REMOVE MOST OF THE ANNOYING ADS from your screen. CLICK HERE TO CREATE YOUR ACCOUNT - Get advice

=== cluster 1 (pool n=27746) ===
  Tallahassee, FL (Sports Network) - An appearance in the 2013 MEAC-SWAC Challenge, a trip to Ohio State and five home games highlight the Florida A&M football schedule announced on Thursday. The Rattlers, who have a new head coach in Earl Holmes, will play 12 games, including five at home for the
  - TV & Video - Renovation & Decorating - How To - Be Healthy - home beautiful You can also make one large shakshuka to share. Just spoon the sauce into a large ovenproof ceramic dish. Crack eggs into the dish, spaced evenly apart, then cook following the same recipe. 1.
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "K = 8              # register clusters", "newString": "K = int(os.environ.get(\"CURATE_K\", \"8\"))   # register clusters", "originalFile": null, "structuredPatch": [{"oldStart": 37, "oldLines": 7, "newStart": 37, "newLines": 7, "lines": [" MIN_TOK = 64", " NEG_SAMPLE = 40000", " EPOCHS = 60", "-K = 8              # register clusters", "+K = int(os.environ.get(\"CURATE_K\", \"8\"))   # register clusters", " TOPN = 3000        # vocab size for clustering histograms", " FILL_TOKENS = 24_000_000   # emit priority order covering ~2x the 12M budget", " t0 = time.time()"]}], "userModified": false, "replaceAll": false}

target cluster proportions: [0.03  0.259 0.008 0.051 0.004 0.001 0.02  0.    0.035 0.169 0.001 0.003
emitted 36492 ids, 24000133 tokens across clusters; per-cluster tokens: [0.044 0.071 0.022 0.064 0.017 0.014 0.033 0.    0.048 0.182 0.014 0.016
wrote /workspace/submission/selection.json  (20.3s)
HELDOUT_PPL 324.042  loss 5.7809  train_s 56  /tmp/k16_train.npy
3998

[stdout]
target cluster proportions: [0.03  0.259 0.008 0.051 0.004 0.001 0.02  0.    0.035 0.169 0.001 0.003
emitted 36492 ids, 24000133 tokens across clusters; per-cluster tokens: [0.044 0.071 0.022 0.064 0.017 0.014 0.033 0.    0.048 0.182 0.014 0.016
wrote /workspace/submission/selection.json  (20.3s)
HELDOUT_PPL 324.042  loss 5.7809  train_s 56  /tmp/k16_train.npy
3998
[metadata] {"noOutputExpected": false}

K=10  HELDOUT_PPL 317.038  loss 5.7590  train_s 56  /tmp/k10_train.npy
3788

[stdout]
K=10  HELDOUT_PPL 317.038  loss 5.7590  train_s 56  /tmp/k10_train.npy
3788
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "valid = (doclen >= MIN_TOK) & (doclen <= 20000) & (uniq_ratio >= 0.35) & (maxfreq <= 0.18)", "newString": "UR = float(os.environ.get(\"CURATE_UR\", \"0.35\")); MF = float(os.environ.get(\"CURATE_MF\", \"0.18\"))\nvalid = (doclen >= MIN_TOK) & (doclen <= 20000) & (uniq_ratio >= UR) & (maxfreq <= MF)", "originalFile": null, "structuredPatch": [{"oldStart": 136, "oldLines": 7, "newStart": 136, "newLines": 8, "lines": ["     uniq_ratio = np.load(\"/tmp/pool_uniqratio.npy\"); maxfreq = np.load(\"/tmp/pool_maxfreq.npy\")", " else:", "     uniq_ratio, maxfreq = rep_stats(); np.save(\"/tmp/pool_uniqratio.npy\", uniq_ratio); np.save(\"/tmp/pool_maxfreq.npy\", maxfreq)", "-valid = (doclen >= MIN_TOK) & (doclen <= 20000) & (uniq_ratio >= 0.35) & (maxfreq <= 0.18)", "+UR = float(os.environ.get(\"CURATE_UR\", \"0.35\")); MF = float(os.environ.get(\"CURATE_MF\", \"0.18\"))", "+valid = (doclen >= MIN_TOK) & (doclen <= 20000) & (uniq_ratio >= UR) & (maxfreq <= MF)", " print(f\"quality prefilter keeps {int(valid.sum())}/{ndoc}\")", " ", " # ---------------- cluster into registers, get target proportions ----------------"]}], "userModified": false, "replaceAll": false}

quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
quality prefilter keeps 118960/182016
K=8 tight  HELDOUT_PPL 346.174  loss 5.8469  train_s 55  /tmp/k8tight_train.npy
3658

[stdout]
quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
quality prefilter keeps 118960/182016
K=8 tight  HELDOUT_PPL 346.174  loss 5.8469  train_s 55  /tmp/k8tight_train.npy
3658
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "MIN_TOK = 64", "newString": "MIN_TOK = int(os.environ.get(\"CURATE_MINTOK\", \"64\"))", "originalFile": null, "structuredPatch": [{"oldStart": 34, "oldLines": 7, "newStart": 34, "newLines": 7, "lines": [" OUT = \"/workspace/submission/selection.json\"", " VOCAB, EOS = 50257, 50256", " D_BIG = 1 << 20", "-MIN_TOK = 64", "+MIN_TOK = int(os.environ.get(\"CURATE_MINTOK\", \"64\"))", " NEG_SAMPLE = 40000", " EPOCHS = 60", " K = int(os.environ.get(\"CURATE_K\", \"8\"))   # register clusters"]}], "userModified": false, "replaceAll": false}

quality prefilter keeps 118413/182016
emitted 31409 ids, 24000274 tokens across clusters; per-cluster tokens: [0.138 0.146 0.35  0.037 0.04  0.084 0.205 0.   ]
K=8 minTok200  HELDOUT_PPL 316.675  loss 5.7579  train_s 57  /tmp/mt200_train.npy
3538

[stdout]
quality prefilter keeps 118413/182016
emitted 31409 ids, 24000274 tokens across clusters; per-cluster tokens: [0.138 0.146 0.35  0.037 0.04  0.084 0.205 0.   ]
K=8 minTok200  HELDOUT_PPL 316.675  loss 5.7579  train_s 57  /tmp/mt200_train.npy
3538
[metadata] {"noOutputExpected": false}

quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
emitted 36827 ids, 24000994 tokens across clusters; per-cluster tokens: [0.136 0.166 0.375 0.03  0.033 0.087 0.174 0.   ]
wrote /workspace/submission/selection.json  (22.3s)
n ids 36827 unique 36827 all in range True
total tokens covered (with EOS): 24037821 >= 12M: True
FINAL  HELDOUT_PPL 315.756  loss 5.7550  train_s 58  /tmp/final_train.npy
3428

[stdout]
quality prefilter keeps 148920/182016
target cluster proportions: [0.107 0.137 0.346 0.002 0.004 0.259 0.145 0.   ]
emitted 36827 ids, 24000994 tokens across clusters; per-cluster tokens: [0.136 0.166 0.375 0.03  0.033 0.087 0.174 0.   ]
wrote /workspace/submission/selection.json  (22.3s)
n ids 36827 unique 36827 all in range True
total tokens covered (with EOS): 24037821 >= 12M: True
FINAL  HELDOUT_PPL 315.756  loss 5.7550  train_s 58  /tmp/final_train.npy
3428
[metadata] {"noOutputExpected": false}

launched official dev_eval pid 3463

[stdout]
launched official dev_eval pid 3463
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/claim.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/claim.md", "content": "# Curation claim\n\n## Hypothesis\nHeld-out perplexity on a broad, multi-domain high-quality target (Wikipedia +\ngeneral web prose + news + technical Q&A) is minimized not by picking the\n*individually highest-quality* pool documents, but by making the **training\nmixture's register distribution match the target's** — at the highest quality the\npool allows within each register. A single quality score collapses the selection\nonto whichever register is easiest to match (here: news), starving the others;\nexplicitly matching the target's register proportions fixes this.\n\nConcretely, the selection is produced by `curate.py`:\n1. **Quality/domain classifier** — a linear model over hashed GPT-2 token\n   unigram+bigram features, trained to separate the disclosed dev target\n   (positives) from random pool docs (negatives). Scores every pool doc for\n   \"target-likeness.\"\n2. **Repetition prefilter** — Gopher/CCNet-style: drop docs with low unique-token\n   ratio or a single token dominating (kills boilerplate like repeated\n   \"Index of /...\" directory listings, which otherwise score *highest* because\n   they are whitespace-heavy and whitespace tokens carry positive weight).\n3. **Distribution matching** — spherical k-means (K=8) on tf-idf token\n   histograms clusters the pool into registers; the dev target is assigned to the\n   same clusters, giving a target token-proportion per cluster. A greedy\n   proportional interleave emits ids so that at every prefix the cluster mix\n   tracks the target's, ordered within each cluster by classifier score.\n\n## Mechanism / prediction (observable other than final perplexity)\nMechanism: matching the target register mix reduces train/target distribution\nmismatch, so the model spends capacity on the registers the target actually\ncontains instead of over-fitting the pool-dominant ones.\n\nPredicted observables (measured on the dev target, all confirmed before the final\nscore):\n- The pool's register mix is **skewed away** from the target: clusters holding\n  29k/45k pool docs receive ~0% / ~11% of target mass, while a target-heavy\n  register (~26% of target tokens) is pool-scarce (~5.5k docs). So a\n  uniform/random draw over-samples off-target registers → higher perplexity.\n- Ranking by the quality classifier alone and taking the top-K **collapses onto\n  one register** (news); its dev perplexity (405) sits between random (457) and\n  distribution-matched (316). Adding register balance is what moves the needle.\n- Ordered map: **random 457 → quality-top 405 → distribution-matched 316**\n  (≈31% below the random baseline). K∈{8,10} tie (~316); coarser K=6 (330) and\n  finer K=16 (324) are worse — over/under-fragmentation both hurt the match.\n\n## Falsification\nThe claim is false if any hold:\n- A **register-balanced** selection does **not** beat a pure quality-top\n  selection of the same token budget (it does: 316 < 405).\n- **Tightening quality** while holding the mixture *improves* perplexity — i.e.\n  quality, not distribution, is the binding constraint. (Tested: tighter filter\n  raised PPL to 346, so over-filtering hurts — distribution/coverage binds, not\n  residual quality.)\n- Matching the target distribution *increases* mismatch on the hidden target\n  (i.e. dev cluster proportions are pure sampling noise and don't estimate the\n  true equal-parts mixture). If so, distribution matching would not transfer.\n\n## Transfer\nThe method needs only (a) a disclosed or estimable target sample and (b) the raw\npool; it is tokenizer-agnostic and classifier-light (a linear bag-of-ngrams, no\nGPU model, no labels). It should transfer to any fixed-budget pretraining\ncuration where the target is a *mixture* of registers and the pool's native mix\ndiffers from it — the failure mode it prevents (a scalar quality filter starving\nminority target registers) is generic. It will **not** help when the pool is\nalready distributed like the target (nothing to rebalance) or when a single\nregister so dominates the target that quality-only selection already matches it.\n```\nrandom baseline PPL 457.2  →  quality-classifier top 405.3  →  distribution-matched (final) 315.8\n```\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (1857 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 36827 ids -> /tmp/dev_train.npy
---timer---
3358

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (1857 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 36827 ids -> /tmp/dev_train.npy
---timer---
3358
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 315.756  loss 5.7550  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
---timer---
3277

[stdout]
HELDOUT_PPL 315.756  loss 5.7550  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
---timer---
3277
[metadata] {"noOutputExpected": false}

=== deliverables ===
-rw-r--r-- 1 root root   4149 Aug  4 03:56 claim.md
-rw-r--r-- 1 root root  10200 Aug  4 03:52 curate.py
-rw-r--r-- 1 root root 262920 Aug  4 03:54 submission/selection.json

=== selection.json head ===
type list | n= 36827 | first 8: [67417, 7456, 42488, 27085, 77020, 68804, 26420, 99338]

=== curate.py reproduces from pool.jsonl (rebuilds caches if missing): stated criterion in docstring ===
"""Curate the raw web pool for small-LM pretraining (reproducible).

Criterion (stated): select pool documents so the training mix (a) is high quality
(junk/boilerplate removed) and (b) MATCHES THE DISCLOSED TARGET DISTRIBUTION —
broad multi-domain HQ English (Wikipedia + general web prose + news + technical

[stdout]
=== deliverables ===
-rw-r--r-- 1 root root   4149 Aug  4 03:56 claim.md
-rw-r--r-- 1 root root  10200 Aug  4 03:52 curate.py
-rw-r--r-- 1 root root 262920 Aug  4 03:54 submission/selection.json

=== selection.json head ===
type list | n= 36827 | first 8: [67417, 7456, 42488, 27085, 77020, 68804, 26420, 99338]

=== curate.py reproduces from pool.jsonl (rebuilds caches if missing): stated criterion in docstring ===
"""Curate the raw web pool for small-LM pretraining (reproducible).

Criterion (stated): select pool documents so the training mix (a) is high quality
(junk/boilerplate removed) and (b) MATCHES THE DISCLOSED TARGET DISTRIBUTION —
broad multi-domain HQ English (Wikipedia + general web prose + news + technical
[metadata] {"noOutputExpected": false}