nano-gpt-data-curation
claude-opus-5
nano-gpt-data-curation — graded by the task's verifier against a measured baseline.
- backend
- modal
- variant
- claude claude-opus-5
- metric
- held-out multi-domain perplexity
- actions
- 87
- duration
- 1h 9m
- recorded
- 2026-08-04
run iddclm-nano-curation__ZPqRAkS
Everything claude-opus-5 did on this task, in order.
download JSON ↗claude-codeclaude-opus-586 turns50 narration87 action87 observation36 reasoning1 final
#26
narration
Weight 3 overshoots (318.4) — optimum bracketed at ~2×. Launching greedy + a 1.5× variant + the fixed diagnostic:
toolBashLaunch greedy, 1.5x qa, and diagnostic runs
args
{
"command": "cd /workspace; cp work/diag_train.py ./diag_train.py; python work/build_sel.py --out cand/balqa15.json --mode balanced --weights 1,1,1,1.5 2>&1|tail -1\npython work/mypack.py cand/greedy.json /tmp/tr_greedy.npy >/dev/null\npython work/mypack.py cand/balqa15.json /tmp/tr_balqa15.npy >/dev/null\nnohup python diag_train.py --train_npy /tmp/tr_balqa.npy --heldout_npy data/multi_dev.npy --out_json /tmp/diag.json > work/run_diag.log 2>&1 &\nnohup python train_nano.py --train_npy /tmp/tr_greedy.npy --heldout_npy data/multi_dev.npy --out_json /tmp/greedy.json > work/run_greedy.log 2>&1 &\nnohup python train_nano.py --train_npy /tmp/tr_balqa15.npy --heldout_npy data/multi_dev.npy --out_json /tmp/balqa15.json > work/run_balqa15.log 2>&1 &\necho launched3; cat .timer/remaining_secs",
"description": "Launch greedy, 1.5x qa, and diagnostic runs"
}Bash result
selected 14920 docs 15000379 tokens -> cand/balqa15.json
launched3
4699
[stdout]
selected 14920 docs 15000379 tokens -> cand/balqa15.json
launched3
4699
[metadata] {"noOutputExpected": false}#27
narration
78 min left. While these train, let me verify my cached tokenization matches the official packer exactly (so my evals reflect the real 12M cut):
toolBashVerify tokenization parity; check runs
args
{
"command": "cd /workspace; python - <<'EOF'\nimport json, numpy as np\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained(\"gpt2\")\nflat=np.load(\"work/flat.npy\"); off=np.load(\"work/off.npy\")\nimport itertools\ncheck=[0,5,1000,50000,181000]\ntxt={}\nfor line in open(\"data/pool.jsonl\"):\n r=json.loads(line)\n if r[\"id\"] in check: txt[r[\"id\"]]=r[\"text\"]\n if len(txt)==len(check): break\nallok=True\nfor i in check:\n a=tok(txt[i],add_special_tokens=False).input_ids\n b=flat[off[i]:off[i+1]].astype(int).tolist()\n if a!=b: allok=False; print(\"MISMATCH\",i,len(a),len(b))\nprint(\"tokenization identical to official packer:\",allok)\nEOF\nsleep 40; for c in diag greedy balqa15; do echo \"== $c\"; cat work/run_$c.log; done",
"description": "Verify tokenization parity; check runs"
}Bash result
Token indices sequence length is longer than the specified maximum sequence length for this model (5621 > 1024). Running this sequence through the model will result in indexing errors
tokenization identical to official packer: True
== diag
== greedy
== balqa15
[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (5621 > 1024). Running this sequence through the model will result in indexing errors
tokenization identical to official packer: True
== diag
== greedy
== balqa15
[metadata] {"noOutputExpected": false}#28
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect run results
args
{
"command": "cd /workspace; sleep 110; for c in diag greedy balqa15; do echo \"== $c\"; cat work/run_$c.log; done; cat .timer/remaining_secs",
"description": "Collect run results"
}Bash result
== diag
SEG 0 wiki loss 6.564 ppl 709.12
SEG 1 webprose loss 5.5778 ppl 264.48
SEG 2 news loss 5.7347 ppl 309.41
SEG 3 qa loss 5.0386 ppl 154.26
HELDOUT_PPL 307.899 loss 5.7298 train_s 161 /tmp/tr_balqa.npy
== greedy
HELDOUT_PPL 279.883 loss 5.6344 train_s 161 /tmp/tr_greedy.npy
== balqa15
HELDOUT_PPL 307.159 loss 5.7274 train_s 161 /tmp/tr_balqa15.npy
4509
[stdout]
== diag
SEG 0 wiki loss 6.564 ppl 709.12
SEG 1 webprose loss 5.5778 ppl 264.48
SEG 2 news loss 5.7347 ppl 309.41
SEG 3 qa loss 5.0386 ppl 154.26
HELDOUT_PPL 307.899 loss 5.7298 train_s 161 /tmp/tr_balqa.npy
== greedy
HELDOUT_PPL 279.883 loss 5.6344 train_s 161 /tmp/tr_greedy.npy
== balqa15
HELDOUT_PPL 307.159 loss 5.7274 train_s 161 /tmp/tr_balqa15.npy
4509
[metadata] {"noOutputExpected": false}#29
narration
Crucial finding from the per-segment breakdown — my assumption was backwards. The WikiText quarter is by far the hardest (ppl 709 vs 154 for Q&A), because its tokenizer artifacts ( , , @-@ ) never occur in raw web text. And greedy distribution-matching is the new best at 279.9. Let me check whether the pool has any such text:
toolBashFind wiki-quarter distinctive tokens vs pool availability
args
{
"command": "cd /workspace; python - <<'EOF'\nimport numpy as np, json\nflat=np.load(\"work/flat.npy\"); off=np.load(\"work/off.npy\"); lens=np.load(\"work/lens.npy\")\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained(\"gpt2\")\ndev=np.load(\"data/multi_dev.npy\").astype(np.int64)\nV=50257\n# which tokens are most over-represented in the wiki quarter vs pool?\nbg=np.bincount(flat.astype(np.int64),minlength=V).astype(np.float64); p_bg=(bg+.5)/(bg.sum()+.5*V)\nw=np.bincount(dev[:250000],minlength=V).astype(np.float64); p_w=(w+.5)/(w.sum()+.5*V)\nr=p_w/p_bg\ntop=np.argsort(-r*(w>50))[:22]\nprint(\"tokens most over-represented in wiki quarter (ratio, wiki count):\")\nfor t in top: print(f\" {repr(tok.decode([int(t)]))!s:>14} ratio {r[t]:7.1f} cnt {int(w[t])}\")\n# how much pool mass has the ' ,' convention?\nfor tid,nm in [(837,\" ','\"),(764,\" '.'\"),(2488,\" '@'\")]:\n c=(flat==tid).sum(); print(nm,\"pool count\",int(c),\"per-100k\",round(1e5*c/len(flat),2),\n \"| wiki-quarter per-100k\",round(1e5*w[tid]/250000,2))\nEOF",
"description": "Find wiki-quarter distinctive tokens vs pool availability"
}Bash result
tokens most over-represented in wiki quarter (ratio, wiki count):
' ,' ratio 231.4 cnt 11582
' Shack' ratio 221.8 cnt 115
' airborne' ratio 155.2 cnt 53
' Monroe' ratio 132.2 cnt 181
' Pokémon' ratio 130.9 cnt 70
' Caesar' ratio 108.3 cnt 65
' Jupiter' ratio 91.5 cnt 83
'landers' ratio 86.5 cnt 51
' Infantry' ratio 84.0 cnt 85
' .' ratio 79.7 cnt 8185
' Titan' ratio 70.7 cnt 78
' Bush' ratio 65.0 cnt 333
' ;' ratio 64.3 cnt 382
' Burton' ratio 63.9 cnt 54
' @' ratio 60.8 cnt 2407
' 000' ratio 56.0 cnt 144
' Hollow' ratio 55.3 cnt 68
'leton' ratio 53.8 cnt 115
' Rome' ratio 48.9 cnt 149
' Pittsburgh' ratio 48.2 cnt 111
' PV' ratio 48.1 cnt 59
'@' ratio 40.7 cnt 2407
',' pool count 36370 per-100k 18.19 | wiki-quarter per-100k 4632.8
'.' pool count 74628 per-100k 37.33 | wiki-quarter per-100k 3274.0
'@' pool count 28752 per-100k 14.38 | wiki-quarter per-100k 962.8
[stdout]
tokens most over-represented in wiki quarter (ratio, wiki count):
' ,' ratio 231.4 cnt 11582
' Shack' ratio 221.8 cnt 115
' airborne' ratio 155.2 cnt 53
' Monroe' ratio 132.2 cnt 181
' Pokémon' ratio 130.9 cnt 70
' Caesar' ratio 108.3 cnt 65
' Jupiter' ratio 91.5 cnt 83
'landers' ratio 86.5 cnt 51
' Infantry' ratio 84.0 cnt 85
' .' ratio 79.7 cnt 8185
' Titan' ratio 70.7 cnt 78
' Bush' ratio 65.0 cnt 333
' ;' ratio 64.3 cnt 382
' Burton' ratio 63.9 cnt 54
' @' ratio 60.8 cnt 2407
' 000' ratio 56.0 cnt 144
' Hollow' ratio 55.3 cnt 68
'leton' ratio 53.8 cnt 115
' Rome' ratio 48.9 cnt 149
' Pittsburgh' ratio 48.2 cnt 111
' PV' ratio 48.1 cnt 59
'@' ratio 40.7 cnt 2407
',' pool count 36370 per-100k 18.19 | wiki-quarter per-100k 4632.8
'.' pool count 74628 per-100k 37.33 | wiki-quarter per-100k 3274.0
'@' pool count 28752 per-100k 14.38 | wiki-quarter per-100k 962.8
[metadata] {"noOutputExpected": false}#30
narration
, and . alone are 8.9% of the wiki quarter's tokens but ~0.05% of the pool — that's where the 709 ppl comes from. These are trivially learnable if any pool doc uses that convention. Let me hunt for them:
toolBashFind pool docs using space-before-punctuation convention
args
{
"command": "cd /workspace; python - <<'EOF'\nimport numpy as np, json\nflat=np.load(\"work/flat.npy\"); off=np.load(\"work/off.npy\"); lens=np.load(\"work/lens.npy\")\nV=50257\nart=np.zeros(V,bool)\nfor t in (837,764,2488,26,5099,838): # ' ,' ' .' ' @' ' ;' ' :' ' )'... check ids\n art[t]=True\nart_only=np.zeros(V,bool); art_only[837]=True; art_only[764]=True\nm=art_only[flat.astype(np.int64)]\ncnt=np.add.reduceat(m.astype(np.float64),off[:-1])\nfrac=cnt/lens\no=np.argsort(-frac)\nprint(\"docs with >=1% of tokens being ' ,'/' .':\", int((frac>=0.01).sum()), \"tokens:\", int(lens[frac>=0.01].sum()))\nprint(\"docs with >=3%:\", int((frac>=0.03).sum()), \"tokens:\", int(lens[frac>=0.03].sum()))\nprint(\"docs with >=5%:\", int((frac>=0.05).sum()), \"tokens:\", int(lens[frac>=0.05].sum()))\nwant=[int(x) for x in o[:6]]\ntxt={}\nfor line in open(\"data/pool.jsonl\"):\n r=json.loads(line)\n if r[\"id\"] in want: txt[r[\"id\"]]=r[\"text\"]\n if len(txt)==len(want): break\nfor i in want:\n print(f\"\\n=== id {i} ntok {int(lens[i])} artifact-frac {frac[i]:.3f}\")\n print(repr(txt[i][:300]))\nEOF",
"description": "Find pool docs using space-before-punctuation convention"
}Bash result
docs with >=1% of tokens being ' ,'/' .': 1455 tokens: 2490239
docs with >=3%: 279 tokens: 362863
docs with >=5%: 90 tokens: 167206
=== id 178386 ntok 2398 artifact-frac 0.306
'c) 2013 turbonuke<|endoftext|>Keranjang Belanja - MWN\nEnglish\nEnglish\nEnglish\nLogin\nDaftar\nLihat Keranjang Belanja\nToggle navigation\nClient Area\nStore\nBrowse All\n-----\nShared Hosting Linux (cPanel/WHM)\nWOLFPRESS (WordPress Hosting)\nShared Hosting Linux (Plesk)\nShared Hosting Linux (Spanel)\nMWN Cloud'
=== id 164681 ntok 1692 artifact-frac 0.300
' with JavaScript enabled<|endoftext|>Shopping Cart - supportHQ.net\nSupportHQ - web hosting\nHome\nFeatures\nPlans\nShared Hosting Plans\nLifetime Hosting Plan\nFAQ\nAbout\n100% Wind Powered\nContact\nClients\nShopping Cart Please login or register\nHome\nAnnouncements\nKnowledgebase\nNetwork Status\nAffiliates\nCont'
=== id 159085 ntok 3163 artifact-frac 0.287
' LokuraNetworks\nDOMINIOS\nHosting\nCloud Hosting SSD\nCloud VPS SSD\nCloud Dedicado SSD\nCloudFlare\nDatacenter\nSERVICIOS\nINTERNET\nIPTV\nStreaming\nTELEFONÍA\nBlog\nContacto\nChoose language\nالعربية\nAzerbaijani\nCatalà\n中文\nHrvatski\nČeština\nDansk\nNederlands\nEnglish\nEstonian\nPersian\nFrançais\nDeutsch\nעברית\nMagyar\nI'
=== id 136429 ntok 3163 artifact-frac 0.287
'.<|endoftext|>WHMCS-bridge – LokuraNetworks\nDOMINIOS\nHosting\nCloud Hosting SSD\nCloud VPS SSD\nCloud Dedicado SSD\nCloudFlare\nDatacenter\nSERVICIOS\nINTERNET\nIPTV\nStreaming\nTELEFONÍA\nBlog\nContacto\nChoose language\nالعربية\nAzerbaijani\nCatalà\n中文\nHrvatski\nČeština\nDansk\nNederlands\nEnglish\nEstonian\nPersian\nFra'
=== id 105413 ntok 153 artifact-frac 0.235
' Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . . Spent two seasons with the Ganchos . . . Had 68 total tackles and an interception in 2012 . . . Totaled 41 stops in 2011 . . . Played prep football at Inkster High School . . . Coached by '
=== id 102634 ntok 451 artifact-frac 0.140
' Yahoo Beauty.<|endoftext|>2007-2008: Enters her junior year fully healed from a knee injury suffered her senior year in high school . . . has made tremendous strides in improving her game over the summer . . . is extremely dedicated to becoming a factor in the Spartans’ plans . . . is a tough playe'
[stdout]
docs with >=1% of tokens being ' ,'/' .': 1455 tokens: 2490239
docs with >=3%: 279 tokens: 362863
docs with >=5%: 90 tokens: 167206
=== id 178386 ntok 2398 artifact-frac 0.306
'c) 2013 turbonuke<|endoftext|>Keranjang Belanja - MWN\nEnglish\nEnglish\nEnglish\nLogin\nDaftar\nLihat Keranjang Belanja\nToggle navigation\nClient Area\nStore\nBrowse All\n-----\nShared Hosting Linux (cPanel/WHM)\nWOLFPRESS (WordPress Hosting)\nShared Hosting Linux (Plesk)\nShared Hosting Linux (Spanel)\nMWN Cloud'
=== id 164681 ntok 1692 artifact-frac 0.300
' with JavaScript enabled<|endoftext|>Shopping Cart - supportHQ.net\nSupportHQ - web hosting\nHome\nFeatures\nPlans\nShared Hosting Plans\nLifetime Hosting Plan\nFAQ\nAbout\n100% Wind Powered\nContact\nClients\nShopping Cart Please login or register\nHome\nAnnouncements\nKnowledgebase\nNetwork Status\nAffiliates\nCont'
=== id 159085 ntok 3163 artifact-frac 0.287
' LokuraNetworks\nDOMINIOS\nHosting\nCloud Hosting SSD\nCloud VPS SSD\nCloud Dedicado SSD\nCloudFlare\nDatacenter\nSERVICIOS\nINTERNET\nIPTV\nStreaming\nTELEFONÍA\nBlog\nContacto\nChoose language\nالعربية\nAzerbaijani\nCatalà\n中文\nHrvatski\nČeština\nDansk\nNederlands\nEnglish\nEstonian\nPersian\nFrançais\nDeutsch\nעברית\nMagyar\nI'
=== id 136429 ntok 3163 artifact-frac 0.287
'.<|endoftext|>WHMCS-bridge – LokuraNetworks\nDOMINIOS\nHosting\nCloud Hosting SSD\nCloud VPS SSD\nCloud Dedicado SSD\nCloudFlare\nDatacenter\nSERVICIOS\nINTERNET\nIPTV\nStreaming\nTELEFONÍA\nBlog\nContacto\nChoose language\nالعربية\nAzerbaijani\nCatalà\n中文\nHrvatski\nČeština\nDansk\nNederlands\nEnglish\nEstonian\nPersian\nFra'
=== id 105413 ntok 153 artifact-frac 0.235
' Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . . Spent two seasons with the Ganchos . . . Had 68 total tackles and an interception in 2012 . . . Totaled 41 stops in 2011 . . . Played prep football at Inkster High School . . . Coached by '
=== id 102634 ntok 451 artifact-frac 0.140
' Yahoo Beauty.<|endoftext|>2007-2008: Enters her junior year fully healed from a knee injury suffered her senior year in high school . . . has made tremendous strides in improving her game over the summer . . . is extremely dedicated to becoming a factor in the Spartans’ plans . . . is a tough playe'
[metadata] {"noOutputExpected": false}#31
narration
The artifact-rich docs are junk boilerplate (hosting nav, sports ellipses) — no WikiText-style prose exists in the pool. But my greedy matcher's ratio clip controls exactly how hard it chases such tokens. Let me check what greedy picked and bracket that knob:
toolBashAnalyze greedy composition; build clip variants
args
{
"command": "cd /workspace; python - <<'EOF'\nimport numpy as np, json\nflat=np.load(\"work/flat.npy\"); off=np.load(\"work/off.npy\"); lens=np.load(\"work/lens.npy\")\nf=flat.astype(np.int64)\nm=((f==837)|(f==764)).astype(np.float64)\ncnt=np.add.reduceat(m,off[:-1]); frac=cnt/lens\nfor name in [\"greedy\",\"balqa\",\"random\"]:\n sel=json.load(open(f\"cand/{name}.json\"))\n c=np.cumsum(lens[sel]+1); k=int(np.searchsorted(c,12_000_000))+1\n s=np.array(sel[:k])\n tot=lens[s].sum()\n art=(m[np.concatenate([np.arange(off[i],off[i+1]) for i in s[frac[s]>0.01]])].sum() if (frac[s]>0.01).any() else 0)\n print(f\"{name:8s} docs {k:6d} artifact-rich(>1%) docs {int((frac[s]>0.01).sum()):5d} \"\n f\"tokens {int(lens[s][frac[s]>0.01].sum()):8d} ({100*lens[s][frac[s]>0.01].sum()/tot:.2f}% of budget) \"\n f\"| overall ' ,'+' .' per-100k {1e5*m[np.concatenate([np.arange(off[i],off[i+1]) for i in s[:400]])].sum()/lens[s[:400]].sum():.0f}\")\nEOF\nfor cl in 6 100; do nohup python work/greedy.py --out cand/g_c$cl.json --clip $cl > work/g_c$cl.log 2>&1 & done; echo building",
"description": "Analyze greedy composition; build clip variants"
}Bash result
greedy docs 11694 artifact-rich(>1%) docs 448 tokens 592260 (4.94% of budget) | overall ' ,'+' .' per-100k 1276
balqa docs 12025 artifact-rich(>1%) docs 95 tokens 104556 (0.87% of budget) | overall ' ,'+' .' per-100k 92
random docs 10934 artifact-rich(>1%) docs 83 tokens 118782 (0.99% of budget) | overall ' ,'+' .' per-100k 51
building
[stdout]
greedy docs 11694 artifact-rich(>1%) docs 448 tokens 592260 (4.94% of budget) | overall ' ,'+' .' per-100k 1276
balqa docs 12025 artifact-rich(>1%) docs 95 tokens 104556 (0.87% of budget) | overall ' ,'+' .' per-100k 92
random docs 10934 artifact-rich(>1%) docs 83 tokens 118782 (0.99% of budget) | overall ' ,'+' .' per-100k 51
building
[metadata] {"noOutputExpected": false}#32
narration
Confirmed the mechanism: greedy spent 4.94% of budget on artifact-rich docs vs 0.99% for random, and its head has 25× the ,/ . density. Launching clip variants plus a diagnostic on greedy:
toolBashLaunch clip=6, clip=100, and greedy diagnostic
args
{
"command": "cd /workspace; wait; tail -1 work/g_c6.log; tail -1 work/g_c100.log\nfor cl in 6 100; do python work/mypack.py cand/g_c$cl.json /tmp/tr_gc$cl.npy >/dev/null; nohup python train_nano.py --train_npy /tmp/tr_gc$cl.npy --heldout_npy data/multi_dev.npy --out_json /tmp/gc$cl.json > work/run_gc$cl.log 2>&1 & done\nnohup python diag_train.py --train_npy /tmp/tr_greedy.npy --heldout_npy data/multi_dev.npy --out_json /tmp/dg.json > work/run_dgreedy.log 2>&1 &\necho launched; cat .timer/remaining_secs",
"description": "Launch clip=6, clip=100, and greedy diagnostic"
}Bash result
10 docs 2500 tok 2099277 10.8
10 docs 2500 tok 2141314 10.9
Traceback (most recent call last):
File "/workspace/work/mypack.py", line 6, in <module>
sel=json.load(open(sys.argv[1])); OUT=sys.argv[2]; BUDGET=12_000_000
FileNotFoundError: [Errno 2] No such file or directory: 'cand/g_c6.json'
Traceback (most recent call last):
File "/workspace/work/mypack.py", line 6, in <module>
sel=json.load(open(sys.argv[1])); OUT=sys.argv[2]; BUDGET=12_000_000
FileNotFoundError: [Errno 2] No such file or directory: 'cand/g_c100.json'
launched
4409
[stdout]
10 docs 2500 tok 2099277 10.8
10 docs 2500 tok 2141314 10.9
Traceback (most recent call last):
File "/workspace/work/mypack.py", line 6, in <module>
sel=json.load(open(sys.argv[1])); OUT=sys.argv[2]; BUDGET=12_000_000
FileNotFoundError: [Errno 2] No such file or directory: 'cand/g_c6.json'
Traceback (most recent call last):
File "/workspace/work/mypack.py", line 6, in <module>
sel=json.load(open(sys.argv[1])); OUT=sys.argv[2]; BUDGET=12_000_000
FileNotFoundError: [Errno 2] No such file or directory: 'cand/g_c100.json'
launched
4409
[metadata] {"noOutputExpected": false}#33
narration
The builds were still running (separate shell). Waiting for them:
toolBashPack and launch clip variants
args
{
"command": "cd /workspace; while [ ! -f cand/g_c100.json ] || [ ! -f cand/g_c6.json ]; do sleep 5; done; grep proxy work/g_c6.log work/g_c100.log\nfor cl in 6 100; do python work/mypack.py cand/g_c$cl.json /tmp/tr_gc$cl.npy >/dev/null; nohup python train_nano.py --train_npy /tmp/tr_gc$cl.npy --heldout_npy data/multi_dev.npy --out_json /tmp/gc$cl.json > work/run_gc$cl.log 2>&1 & done; echo launched; cat work/run_dgreedy.log",
"description": "Pack and launch clip variants"
}Bash result
work/g_c6.log:proxy H(p_t,p_sel) = 8.09891224439071
work/g_c100.log:proxy H(p_t,p_sel) = 8.087514466067416
launched
[stdout]
work/g_c6.log:proxy H(p_t,p_sel) = 8.09891224439071
work/g_c100.log:proxy H(p_t,p_sel) = 8.087514466067416
launched
[metadata] {"noOutputExpected": false}#34
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect clip-variant and diagnostic results
args
{
"command": "cd /workspace; sleep 150; echo \"== diag greedy\"; cat work/run_dgreedy.log; echo \"== clip6\"; cat work/run_gc6.log; echo \"== clip100\"; cat work/run_gc100.log; cat .timer/remaining_secs",
"description": "Collect clip-variant and diagnostic results"
}Bash result
Command did not complete within its 120s timeout and was moved to the background (ID: ba8wcozc9). Output is being written to: /tmp/claude-0/-workspace/21ef8abe-2df0-457e-9c7a-a72b69647ba6/tasks/ba8wcozc9.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": "ba8wcozc9", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}#35
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect results
args
{
"command": "cd /workspace; sleep 55; echo \"== diag greedy\"; cat work/run_dgreedy.log; echo \"== clip6\"; cat work/run_gc6.log; echo \"== clip100\"; cat work/run_gc100.log; cat .timer/remaining_secs",
"description": "Collect results"
}Bash result
== diag greedy
SEG 0 wiki loss 6.3078 ppl 548.86
SEG 1 webprose loss 5.5589 ppl 259.53
SEG 2 news loss 5.6297 ppl 278.58
SEG 3 qa loss 5.0375 ppl 154.08
HELDOUT_PPL 279.883 loss 5.6344 train_s 51 /tmp/tr_greedy.npy
== clip6
HELDOUT_PPL 279.894 loss 5.6344 train_s 108 /tmp/tr_gc6.npy
== clip100
HELDOUT_PPL 279.255 loss 5.6321 train_s 108 /tmp/tr_gc100.npy
4169
[stdout]
== diag greedy
SEG 0 wiki loss 6.3078 ppl 548.86
SEG 1 webprose loss 5.5589 ppl 259.53
SEG 2 news loss 5.6297 ppl 278.58
SEG 3 qa loss 5.0375 ppl 154.08
HELDOUT_PPL 279.883 loss 5.6344 train_s 51 /tmp/tr_greedy.npy
== clip6
HELDOUT_PPL 279.894 loss 5.6344 train_s 108 /tmp/tr_gc6.npy
== clip100
HELDOUT_PPL 279.255 loss 5.6321 train_s 108 /tmp/tr_gc100.npy
4169
[metadata] {"noOutputExpected": false}#36
narration
Clip is saturated (279.9 / 279.9 / 279.3) — greedy already extracts what the pool offers there. Wiki loss fell 6.56→6.31 but still dominates. Three distinct hypotheses left; adding two knobs and testing all in parallel:
toolBashAdd artifact-quota and register-weight knobs; build 3 variants
args
{
"command": "cd /workspace; python - <<'PY'\nimport re\ns=open('work/greedy.py').read()\ns=s.replace('ap.add_argument(\"--dedup\",type=int,default=1)',\n 'ap.add_argument(\"--dedup\",type=int,default=1)\\nap.add_argument(\"--tweights\",default=\"1,1,1,1\")\\nap.add_argument(\"--artquota\",type=float,default=0.0)')\ns=s.replace(\"\"\"ct=np.bincount(dev,minlength=V).astype(np.float64)\np_t=(ct+1.0)/(ct.sum()+V) # target unigram (equal-parts mixture)\"\"\",\n\"\"\"TW=np.array([float(x) for x in a.tweights.split(\",\")]); TW/=TW.sum()\nQ4=len(dev)//4\np_t=np.zeros(V)\nfor k in range(4):\n c=np.bincount(dev[k*Q4:(k+1)*Q4],minlength=V).astype(np.float64)\n p_t+=TW[k]*(c+1.0)/(c.sum()+V) # register-weighted target unigram\"\"\")\ns=s.replace(\"\"\"sel=[]; tot=0; seen_doc=set(); seen_sk=set()\nflat64=flat.astype(np.int64)\"\"\",\n\"\"\"sel=[]; tot=0; seen_doc=set(); seen_sk=set()\nflat64=flat.astype(np.int64)\nif a.artquota>0: # explicit quota of docs carrying the target's space-before-punct convention\n art=((flat64==837)|(flat64==764)).astype(np.float64)\n afrac=np.add.reduceat(art,off[:-1])/lens\n q=0\n for j in np.argsort(-afrac):\n j=int(j)\n if lens[j]<a.minlen or afrac[j]<=0.005: continue\n if int(dochash[j]) in seen_doc: continue\n seen_doc.add(int(dochash[j])); seen_sk.update(int(x) for x in sketch[j] if x!=0)\n alive[j]=False; sel.append(j); tot+=int(lens[j])+1; q+=int(lens[j])\n c_sel+=np.bincount(flat64[off[j]:off[j+1]],minlength=V); n_sel+=int(lens[j])\n if q>=a.artquota: break\n print(\"artifact quota tokens\",q,\"docs\",len(sel),flush=True)\"\"\")\nopen('work/greedy.py','w').write(s)\nprint(\"patched\")\nPY\nnohup python work/greedy.py --out cand/g_aq.json --artquota 1.5e6 > work/g_aq.log 2>&1 &\nnohup python work/greedy.py --out cand/g_tw.json --tweights 2,1,1,1 > work/g_tw.log 2>&1 &\nnohup python work/greedy.py --out cand/g_ml.json --minlen 900 > work/g_ml.log 2>&1 &\necho building3",
"description": "Add artifact-quota and register-weight knobs; build 3 variants"
}Bash result
patched
building3
[stdout]
patched
building3
[metadata] {"noOutputExpected": false}#37
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashPack and launch 3 hypothesis runs
args
{
"command": "cd /workspace; for f in g_aq g_tw g_ml; do while [ ! -f cand/$f.json ]; do sleep 4; done; done; grep -h \"quota\\|proxy\\|eligible\" work/g_aq.log work/g_tw.log work/g_ml.log\nfor f in g_aq g_tw g_ml; do python work/mypack.py cand/$f.json /tmp/tr_$f.npy >/dev/null; nohup python train_nano.py --train_npy /tmp/tr_$f.npy --heldout_npy data/multi_dev.npy --out_json /tmp/$f.json > work/run_$f.log 2>&1 & done; echo launched; cat .timer/remaining_secs",
"description": "Pack and launch 3 hypothesis runs"
}Bash result
eligible 99879
artifact quota tokens 1504356 docs 507
proxy H(p_t,p_sel) = 8.628759686726916
eligible 42520
proxy H(p_t,p_sel) = 8.665699426008041
eligible 99879
proxy H(p_t,p_sel) = 8.679136017608975
launched
4078
[stdout]
eligible 99879
artifact quota tokens 1504356 docs 507
proxy H(p_t,p_sel) = 8.628759686726916
eligible 42520
proxy H(p_t,p_sel) = 8.665699426008041
eligible 99879
proxy H(p_t,p_sel) = 8.679136017608975
launched
4078
[metadata] {"noOutputExpected": false}#38
narration
68 min left. Drafting the final self-contained curate.py while these run:
toolWrite
args
{
"file_path": "/workspace/submission/curate.py",
"content": "#!/usr/bin/env python3\n\"\"\"\nCurate /workspace/data/pool.jsonl down to a 12M-token pretraining set for the\nfrozen 30M-param GPT, targeting low perplexity on a broad multi-domain\nhigh-quality English target (encyclopedic / web prose / news / technical Q&A).\n\nSTATED CRITERION\n----------------\nHeld-out perplexity is exp of the mean next-token cross-entropy on the target\nstream. We therefore select the subset of the pool whose *own* token\ndistribution is closest to the target's, measuring closeness with the same\nfunctional the metric uses -- cross-entropy -- at unigram order, which is the\nhighest order estimable from the 1M-token disclosed dev target:\n\n minimise H(p_target, p_selection) = - sum_v p_target(v) log p_selection(v)\n over subsets S of the pool with sum_{d in S} |d| <= budget.\n\nAdding document d moves p_selection along the direction (c_d/|d| - p_selection),\nso the first-order decrease of H from adding d is proportional to\n\n gain(d) = (1/|d|) sum_{t in d} min( p_target(t) / p_selection(t), CLIP )\n\ni.e. the mean *importance ratio* of d's tokens under the current selection.\nWe run batched greedy on this gain: score every eligible document, admit the\nbest BATCH of them, update p_selection, repeat until the budget is covered.\nThe ratio is clipped because some target tokens are unreachable at any price\n(the encyclopedic quarter is WikiText-formatted: ' ,' / ' .' / ' @-@ ' occur at\n~9% of its tokens and are essentially absent from raw web text); without a clip\nthe objective would spend unbounded budget chasing them.\n\nTwo guards keep the objective honest, since a pure frequency-matching score is\nhappy to buy rare tokens from boilerplate:\n * quality gate -- minimum length, and caps on 5-gram repetition / top-token\n share / minimum type-token ratio, which reject nav-bars, link farms and\n directory listings;\n * streaming near-duplicate filter -- 4-permutation MinHash over 5-gram\n shingles; a document sharing >=2 of 4 sketch minima with an already-selected\n document is dropped, so the budget is not spent re-reading the same page.\n\nDocuments are emitted in the order greedy admitted them, so truncating the list\nat the 12M-token budget preserves the matched mixture.\n\nRun: python curate.py (~3 min: tokenise pool, featurise, select)\n\"\"\"\nimport json, os, time\nimport numpy as np\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV = \"/workspace/data/multi_dev.npy\" # disclosed dev target\nOUT = \"/workspace/submission/selection.json\"\nCACHE = \"/workspace/work\" # reused if already present\nV = 50257\nBUDGET = 12_000_000\nTARGET = 15_000_000 # emit ~25% past the budget so truncation is safe\n\n# --- selection hyperparameters (chosen on the disclosed dev target) ---\nCLIP = 100.0 # cap on the per-token importance ratio\nBATCH = 250 # documents admitted per greedy re-scoring step\nPRIOR = 3e5 # pseudo-tokens of pool background seeding p_selection\nMIN_LEN = 300 # tokens; >= block_size(256) so a window can sit inside a doc\nMAX_REP5 = 0.35 # max fraction of duplicated 5-gram shingles\nMAX_TOP1 = 0.12 # max share of the single most frequent token\nMIN_UNIQ = 0.18 # min type-token ratio\nTWEIGHTS = (1., 1., 1., 1.) # relative weight of the 4 target registers\n\nK = 5 # shingle length\nP = np.uint64(1099511628211)\n\n\ndef tokenize_pool():\n \"\"\"GPT-2 BPE over the whole pool -> flat token stream + document offsets.\"\"\"\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n from tokenizers import Tokenizer\n tok = Tokenizer.from_pretrained(\"gpt2\")\n arrs, lens, ids, bt, bi = [], [], [], [], []\n\n def flush():\n if not bt:\n return\n for i, e in zip(bi, tok.encode_batch(bt)):\n arrs.append(np.asarray(e.ids, dtype=np.uint16))\n lens.append(len(e.ids)); ids.append(i)\n bt.clear(); bi.clear()\n\n for line in open(POOL):\n r = json.loads(line); bt.append(r[\"text\"]); bi.append(r[\"id\"])\n if len(bt) >= 4096:\n flush()\n flush()\n lens = np.array(lens, dtype=np.int64)\n off = np.zeros(len(lens) + 1, dtype=np.int64); np.cumsum(lens, out=off[1:])\n return np.concatenate(arrs), off, lens, np.array(ids, dtype=np.int64)\n\n\ndef features(flat, off, lens):\n \"\"\"Per-document junk features and MinHash sketches (5-gram shingles).\"\"\"\n N = len(lens)\n f = flat.astype(np.uint64)\n h = np.zeros(len(f) - K + 1, dtype=np.uint64) # rolling poly hash mod 2^64\n for k in range(K):\n h = h * P + f[k:len(f) - K + 1 + k]\n mixers = [np.uint64(x) for x in (0x9E3779B97F4A7C15, 0xC2B2AE3D27D4EB4F,\n 0x165667B19E3779F9, 0x27D4EB2F165667C5)]\n rep5 = np.zeros(N, np.float32); uniq = np.zeros(N, np.float32)\n top1 = np.zeros(N, np.float32); sketch = np.zeros((N, 4), np.uint64)\n dochash = np.zeros(N, np.uint64)\n for i in range(N):\n s, e = off[i], off[i + 1]\n a = f[s:e]\n if len(a) == 0:\n continue\n dh = np.uint64(len(a))\n for chunk in (a[:32], a[-32:]):\n for v in chunk:\n dh = dh * P + v\n dochash[i] = dh\n u, c = np.unique(a, return_counts=True)\n uniq[i] = len(u) / len(a); top1[i] = c.max() / len(a)\n if e - s >= K + 1:\n g = np.unique(h[s:e - K + 1])\n rep5[i] = 1.0 - len(g) / (e - s - K + 1)\n for j, m in enumerate(mixers):\n sketch[i, j] = (g * m).min()\n return rep5, uniq, top1, sketch.astype(np.int64), dochash.astype(np.int64)\n\n\ndef cached():\n \"\"\"Load cached tokenisation/features if this script already ran once.\"\"\"\n need = [\"flat\", \"off\", \"lens\", \"docids\", \"rep5\", \"uniq\", \"top1\", \"sketch\", \"dochash\"]\n if all(os.path.exists(f\"{CACHE}/{n}.npy\") for n in need):\n return [np.load(f\"{CACHE}/{n}.npy\") for n in need]\n return None\n\n\ndef main():\n t0 = time.time()\n c = cached()\n if c is None:\n flat, off, lens, docids = tokenize_pool()\n print(f\"tokenised {len(lens)} docs, {int(lens.sum())} tokens {time.time()-t0:.0f}s\")\n rep5, uniq, top1, sketch, dochash = features(flat, off, lens)\n os.makedirs(CACHE, exist_ok=True)\n for n, v in zip([\"flat\", \"off\", \"lens\", \"docids\", \"rep5\", \"uniq\", \"top1\",\n \"sketch\", \"dochash\"],\n [flat, off, lens, docids, rep5, uniq, top1, sketch, dochash]):\n np.save(f\"{CACHE}/{n}.npy\", v)\n else:\n flat, off, lens, docids, rep5, uniq, top1, sketch, dochash = c\n print(f\"loaded cached features {time.time()-t0:.0f}s\")\n f64 = flat.astype(np.int64)\n\n # pool background distribution (the \"do nothing\" reference distribution)\n bg = np.bincount(f64, minlength=V).astype(np.float64)\n p_bg = (bg + 0.5) / (bg.sum() + 0.5 * V)\n\n # target unigram: equal-parts mixture of the 4 disclosed registers\n dev = np.load(DEV).astype(np.int64)\n Q = len(dev) // 4\n tw = np.array(TWEIGHTS, dtype=np.float64); tw /= tw.sum()\n p_t = np.zeros(V)\n for k in range(4):\n ck = np.bincount(dev[k * Q:(k + 1) * Q], minlength=V).astype(np.float64)\n p_t += tw[k] * (ck + 1.0) / (ck.sum() + V)\n\n ok = ((lens >= MIN_LEN) & (rep5 <= MAX_REP5) &\n (top1 <= MAX_TOP1) & (uniq >= MIN_UNIQ))\n print(f\"eligible after quality gate: {int(ok.sum())} docs, \"\n f\"{int(lens[ok].sum())} tokens\")\n\n alive = ok.copy()\n c_sel = p_bg * PRIOR; n_sel = PRIOR\n sel, tot = [], 0\n seen_doc, seen_sk = set(), set()\n while tot < TARGET:\n r = np.clip(p_t / (c_sel / n_sel), 0, CLIP).astype(np.float32)\n gain = np.add.reduceat(r[f64].astype(np.float64), off[:-1]) / lens\n gain[~alive] = -1e9\n cand = np.argpartition(-gain, BATCH * 3)[:BATCH * 3]\n cand = cand[np.argsort(-gain[cand])]\n added = 0\n for j in cand:\n j = int(j)\n if not alive[j]:\n continue\n if int(dochash[j]) in seen_doc:\n alive[j] = False; continue\n sk = [int(x) for x in sketch[j] if x != 0]\n if sum(x in seen_sk for x in sk) >= 2: # near-duplicate\n alive[j] = False; continue\n seen_doc.add(int(dochash[j])); seen_sk.update(sk)\n alive[j] = False\n sel.append(j); tot += int(lens[j]) + 1\n c_sel += np.bincount(f64[off[j]:off[j + 1]], minlength=V)\n n_sel += int(lens[j])\n added += 1\n if added >= BATCH or tot >= TARGET:\n break\n if added == 0:\n break\n\n p_sel = c_sel / n_sel\n print(f\"proxy cross-entropy H(p_t,p_sel) = {-(p_t*np.log(p_sel)).sum():.4f} nats \"\n f\"(pool background: {-(p_t*np.log(p_bg)).sum():.4f})\")\n ids = [int(docids[j]) for j in sel]\n assert len(ids) == len(set(ids)), \"duplicate ids\"\n json.dump(ids, open(OUT, \"w\"))\n print(f\"wrote {len(ids)} ids ({tot} tokens, budget {BUDGET}) -> {OUT} \"\n f\"{time.time()-t0:.0f}s\")\n\n\nif __name__ == \"__main__\":\n main()\n"
}Write result
File created successfully at: /workspace/submission/curate.py (file state is current in your context — no need to Read it back)
[metadata] {"type": "create", "filePath": "/workspace/submission/curate.py", "content": "#!/usr/bin/env python3\n\"\"\"\nCurate /workspace/data/pool.jsonl down to a 12M-token pretraining set for the\nfrozen 30M-param GPT, targeting low perplexity on a broad multi-domain\nhigh-quality English target (encyclopedic / web prose / news / technical Q&A).\n\nSTATED CRITERION\n----------------\nHeld-out perplexity is exp of the mean next-token cross-entropy on the target\nstream. We therefore select the subset of the pool whose *own* token\ndistribution is closest to the target's, measuring closeness with the same\nfunctional the metric uses -- cross-entropy -- at unigram order, which is the\nhighest order estimable from the 1M-token disclosed dev target:\n\n minimise H(p_target, p_selection) = - sum_v p_target(v) log p_selection(v)\n over subsets S of the pool with sum_{d in S} |d| <= budget.\n\nAdding document d moves p_selection along the direction (c_d/|d| - p_selection),\nso the first-order decrease of H from adding d is proportional to\n\n gain(d) = (1/|d|) sum_{t in d} min( p_target(t) / p_selection(t), CLIP )\n\ni.e. the mean *importance ratio* of d's tokens under the current selection.\nWe run batched greedy on this gain: score every eligible document, admit the\nbest BATCH of them, update p_selection, repeat until the budget is covered.\nThe ratio is clipped because some target tokens are unreachable at any price\n(the encyclopedic quarter is WikiText-formatted: ' ,' / ' .' / ' @-@ ' occur at\n~9% of its tokens and are essentially absent from raw web text); without a clip\nthe objective would spend unbounded budget chasing them.\n\nTwo guards keep the objective honest, since a pure frequency-matching score is\nhappy to buy rare tokens from boilerplate:\n * quality gate -- minimum length, and caps on 5-gram repetition / top-token\n share / minimum type-token ratio, which reject nav-bars, link farms and\n directory listings;\n * streaming near-duplicate filter -- 4-permutation MinHash over 5-gram\n shingles; a document sharing >=2 of 4 sketch minima with an already-selected\n document is dropped, so the budget is not spent re-reading the same page.\n\nDocuments are emitted in the order greedy admitted them, so truncating the list\nat the 12M-token budget preserves the matched mixture.\n\nRun: python curate.py (~3 min: tokenise pool, featurise, select)\n\"\"\"\nimport json, os, time\nimport numpy as np\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV = \"/workspace/data/multi_dev.npy\" # disclosed dev target\nOUT = \"/workspace/submission/selection.json\"\nCACHE = \"/workspace/work\" # reused if already present\nV = 50257\nBUDGET = 12_000_000\nTARGET = 15_000_000 # emit ~25% past the budget so truncation is safe\n\n# --- selection hyperparameters (chosen on the disclosed dev target) ---\nCLIP = 100.0 # cap on the per-token importance ratio\nBATCH = 250 # documents admitted per greedy re-scoring step\nPRIOR = 3e5 # pseudo-tokens of pool background seeding p_selection\nMIN_LEN = 300 # tokens; >= block_size(256) so a window can sit inside a doc\nMAX_REP5 = 0.35 # max fraction of duplicated 5-gram shingles\nMAX_TOP1 = 0.12 # max share of the single most frequent token\nMIN_UNIQ = 0.18 # min type-token ratio\nTWEIGHTS = (1., 1., 1., 1.) # relative weight of the 4 target registers\n\nK = 5 # shingle length\nP = np.uint64(1099511628211)\n\n\ndef tokenize_pool():\n \"\"\"GPT-2 BPE over the whole pool -> flat token stream + document offsets.\"\"\"\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n from tokenizers import Tokenizer\n tok = Tokenizer.from_pretrained(\"gpt2\")\n arrs, lens, ids, bt, bi = [], [], [], [], []\n\n def flush():\n if not bt:\n return\n for i, e in zip(bi, tok.encode_batch(bt)):\n arrs.append(np.asarray(e.ids, dtype=np.uint16))\n lens.append(len(e.ids)); ids.append(i)\n bt.clear(); bi.clear()\n\n for line in open(POOL):\n r = json.loads(line); bt.append(r[\"text\"]); bi.append(r[\"id\"])\n if len(bt) >= 4096:\n flush()\n flush()\n lens = np.array(lens, dtype=np.int64)\n off = np.zeros(len(lens) + 1, dtype=np.int64); np.cumsum(lens, out=off[1:])\n return np.concatenate(arrs), off, lens, np.array(ids, dtype=np.int64)\n\n\ndef features(flat, off, lens):\n \"\"\"Per-document junk features and MinHash sketches (5-gram shingles).\"\"\"\n N = len(lens)\n f = flat.astype(np.uint64)\n h = np.zeros(len(f) - K + 1, dtype=np.uint64) # rolling poly hash mod 2^64\n for k in range(K):\n h = h * P + f[k:len(f) - K + 1 + k]\n mixers = [np.uint64(x) for x in (0x9E3779B97F4A7C15, 0xC2B2AE3D27D4EB4F,\n 0x165667B19E3779F9, 0x27D4EB2F165667C5)]\n rep5 = np.zeros(N, np.float32); uniq = np.zeros(N, np.float32)\n top1 = np.zeros(N, np.float32); sketch = np.zeros((N, 4), np.uint64)\n dochash = np.zeros(N, np.uint64)\n for i in range(N):\n s, e = off[i], off[i + 1]\n a = f[s:e]\n if len(a) == 0:\n continue\n dh = np.uint64(len(a))\n for chunk in (a[:32], a[-32:]):\n for v in chunk:\n dh = dh * P + v\n dochash[i] = dh\n u, c = np.unique(a, return_counts=True)\n uniq[i] = len(u) / len(a); top1[i] = c.max() / len(a)\n if e - s >= K + 1:\n g = np.unique(h[s:e - K + 1])\n rep5[i] = 1.0 - len(g) / (e - s - K + 1)\n for j, m in enumerate(mixers):\n sketch[i, j] = (g * m).min()\n return rep5, uniq, top1, sketch.astype(np.int64), dochash.astype(np.int64)\n\n\ndef cached():\n \"\"\"Load cached tokenisation/features if this script already ran once.\"\"\"\n need = [\"flat\", \"off\", \"lens\", \"docids\", \"rep5\", \"uniq\", \"top1\", \"sketch\", \"dochash\"]\n if all(os.path.exists(f\"{CACHE}/{n}.npy\") for n in need):\n return [np.load(f\"{CACHE}/{n}.npy\") for n in need]\n return None\n\n\ndef main():\n t0 = time.time()\n c = cached()\n if c is None:\n flat, off, lens, docids = tokenize_pool()\n print(f\"tokenised {len(lens)} docs, {int(lens.sum())} tokens {time.time()-t0:.0f}s\")\n rep5, uniq, top1, sketch, dochash = features(flat, off, lens)\n os.makedirs(CACHE, exist_ok=True)\n for n, v in zip([\"flat\", \"off\", \"lens\", \"docids\", \"rep5\", \"uniq\", \"top1\",\n \"sketch\", \"dochash\"],\n [flat, off, lens, docids, rep5, uniq, top1, sketch, dochash]):\n np.save(f\"{CACHE}/{n}.npy\", v)\n else:\n flat, off, lens, docids, rep5, uniq, top1, sketch, dochash = c\n print(f\"loaded cached features {time.time()-t0:.0f}s\")\n f64 = flat.astype(np.int64)\n\n # pool background distribution (the \"do nothing\" reference distribution)\n bg = np.bincount(f64, minlength=V).astype(np.float64)\n p_bg = (bg + 0.5) / (bg.sum() + 0.5 * V)\n\n # target unigram: equal-parts mixture of the 4 disclosed registers\n dev = np.load(DEV).astype(np.int64)\n Q = len(dev) // 4\n tw = np.array(TWEIGHTS, dtype=np.float64); tw /= tw.sum()\n p_t = np.zeros(V)\n for k in range(4):\n ck = np.bincount(dev[k * Q:(k + 1) * Q], minlength=V).astype(np.float64)\n p_t += tw[k] * (ck + 1.0) / (ck.sum() + V)\n\n ok = ((lens >= MIN_LEN) & (rep5 <= MAX_REP5) &\n (top1 <= MAX_TOP1) & (uniq >= MIN_UNIQ))\n print(f\"eligible after quality gate: {int(ok.sum())} docs, \"\n f\"{int(lens[ok].sum())} tokens\")\n\n alive = ok.copy()\n c_sel = p_bg * PRIOR; n_sel = PRIOR\n sel, tot = [], 0\n seen_doc, seen_sk = set(), set()\n while tot < TARGET:\n r = np.clip(p_t / (c_sel / n_sel), 0, CLIP).astype(np.float32)\n gain = np.add.reduceat(r[f64].astype(np.float64), off[:-1]) / lens\n gain[~alive] = -1e9\n cand = np.argpartition(-gain, BATCH * 3)[:BATCH * 3]\n cand = cand[np.argsort(-gain[cand])]\n added = 0\n for j in cand:\n j = int(j)\n if not alive[j]:\n continue\n if int(dochash[j]) in seen_doc:\n alive[j] = False; continue\n sk = [int(x) for x in sketch[j] if x != 0]\n if sum(x in seen_sk for x in sk) >= 2: # near-duplicate\n alive[j] = False; continue\n seen_doc.add(int(dochash[j])); seen_sk.update(sk)\n alive[j] = False\n sel.append(j); tot += int(lens[j]) + 1\n c_sel += np.bincount(f64[off[j]:off[j + 1]], minlength=V)\n n_sel += int(lens[j])\n added += 1\n if added >= BATCH or tot >= TARGET:\n break\n if added == 0:\n break\n\n p_sel = c_sel / n_sel\n print(f\"proxy cross-entropy H(p_t,p_sel) = {-(p_t*np.log(p_sel)).sum():.4f} nats \"\n f\"(pool background: {-(p_t*np.log(p_bg)).sum():.4f})\")\n ids = [int(docids[j]) for j in sel]\n assert len(ids) == len(set(ids)), \"duplicate ids\"\n json.dump(ids, open(OUT, \"w\"))\n print(f\"wrote {len(ids)} ids ({tot} tokens, budget {BUDGET}) -> {OUT} \"\n f\"{time.time()-t0:.0f}s\")\n\n\nif __name__ == \"__main__\":\n main()\n", "structuredPatch": [], "originalFile": null, "userModified": false}#39
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect hypothesis run results
args
{
"command": "cd /workspace; for f in g_aq g_tw g_ml; do echo -n \"$f: \"; cat work/run_$f.log; echo; done; cat .timer/remaining_secs",
"description": "Collect hypothesis run results"
}Bash result
g_aq:
g_tw:
g_ml:
4028
[stdout]
g_aq:
g_tw:
g_ml:
4028
[metadata] {"noOutputExpected": false}#40
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect results
args
{
"command": "cd /workspace; sleep 100; for f in g_aq g_tw g_ml; do echo -n \"$f: \"; cat work/run_$f.log; echo; done; cat .timer/remaining_secs",
"description": "Collect results"
}Bash result
g_aq:
g_tw:
g_ml:
3918
[stdout]
g_aq:
g_tw:
g_ml:
3918
[metadata] {"noOutputExpected": false}#41
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashCollect results
args
{
"command": "cd /workspace; sleep 60; for f in g_aq g_tw g_ml; do echo -n \"$f: \"; cat work/run_$f.log; echo; done; cat .timer/remaining_secs",
"description": "Collect results"
}Bash result
g_aq: HELDOUT_PPL 290.457 loss 5.6715 train_s 161 /tmp/tr_g_aq.npy
g_tw: HELDOUT_PPL 288.758 loss 5.6656 train_s 161 /tmp/tr_g_tw.npy
g_ml: HELDOUT_PPL 295.417 loss 5.6884 train_s 162 /tmp/tr_g_ml.npy
3858
[stdout]
g_aq: HELDOUT_PPL 290.457 loss 5.6715 train_s 161 /tmp/tr_g_aq.npy
g_tw: HELDOUT_PPL 288.758 loss 5.6656 train_s 161 /tmp/tr_g_tw.npy
g_ml: HELDOUT_PPL 295.417 loss 5.6884 train_s 162 /tmp/tr_g_ml.npy
3858
[metadata] {"noOutputExpected": false}#42
narration
All three perturbations are worse than the unmodified objective (290.5 / 288.8 / 295.4 vs 279.3) — forcing artifact junk, tilting register weights, and demanding long docs all hurt. Locking in a valid submission now, then pursuing upside:
toolBashRun curate.py, verify selection validity
args
{
"command": "cd /workspace; python submission/curate.py 2>&1 | tail -5; python - <<'EOF'\nimport json, numpy as np\na=json.load(open('/workspace/submission/selection.json')); b=json.load(open('/workspace/cand/g_c100.json'))\nprint(\"matches evaluated candidate g_c100:\", a==b, len(a))\nlens=np.load('/workspace/work/lens.npy')\nprint(\"unique:\",len(a)==len(set(a)),\"in range:\",min(a)>=0 and max(a)<182016,\n \"tokens:\",int((lens[a]+1).sum()))\nEOF",
"description": "Run curate.py, verify selection validity"
}Bash result
loaded cached features 0s
eligible after quality gate: 99879 docs, 121898909 tokens
proxy cross-entropy H(p_t,p_sel) = 8.6381 nats (pool background: 8.8249)
wrote 13822 ids (15013006 tokens, budget 12000000) -> /workspace/submission/selection.json 60s
matches evaluated candidate g_c100: False 13822
unique: True in range: True tokens: 15013006
[stdout]
loaded cached features 0s
eligible after quality gate: 99879 docs, 121898909 tokens
proxy cross-entropy H(p_t,p_sel) = 8.6381 nats (pool background: 8.8249)
wrote 13822 ids (15013006 tokens, budget 12000000) -> /workspace/submission/selection.json 60s
matches evaluated candidate g_c100: False 13822
unique: True in range: True tokens: 15013006
[metadata] {"noOutputExpected": false}#43
narration
Small p_t smoothing difference from the tested candidate — so I'll validate the actual submission directly. Launching its run, the official-packer parity check, and two knob variants together:
toolBashValidate submission, check packer parity, build knob variants
args
{
"command": "cd /workspace; python work/mypack.py submission/selection.json /tmp/tr_sub.npy\nnohup python diag_train.py --train_npy /tmp/tr_sub.npy --heldout_npy data/multi_dev.npy --out_json /tmp/sub.json > work/run_sub.log 2>&1 &\nTOKENIZERS_PARALLELISM=false nohup python pack_selection.py submission/selection.json /tmp/official.npy > work/official_pack.log 2>&1 &\nnohup python work/greedy.py --out cand/g_ml200.json --clip 100 --minlen 200 > work/g_ml200.log 2>&1 &\nnohup python work/greedy.py --out cand/g_b100.json --clip 100 --batch 100 > work/g_b100.log 2>&1 &\necho launched; cat .timer/remaining_secs",
"description": "Validate submission, check packer parity, build knob variants"
}Bash result
packed 12000000 tokens from 13822 ids -> /tmp/tr_sub.npy
launched
3748
[stdout]
packed 12000000 tokens from 13822 ids -> /tmp/tr_sub.npy
launched
3748
[metadata] {"noOutputExpected": false}#44
narration
62 min left. The key ablation for claim integrity: does distribution-matching beat just the quality gate + dedup? Building that now:
toolBashBuild quality-gate-only ablation; check running jobs
args
{
"command": "cd /workspace; python - <<'EOF'\nimport numpy as np, json\nlens=np.load('work/lens.npy'); rep5=np.load('work/rep5.npy'); top1=np.load('work/top1.npy')\nuniq=np.load('work/uniq.npy'); dochash=np.load('work/dochash.npy'); sketch=np.load('work/sketch.npy')\nok=(lens>=300)&(rep5<=0.35)&(top1<=0.12)&(uniq>=0.18)\nelig=np.where(ok)[0]; rng=np.random.default_rng(7); rng.shuffle(elig)\nsel=[];tot=0;sd=set();ss=set()\nfor j in elig:\n j=int(j)\n if int(dochash[j]) in sd: continue\n sk=[int(x) for x in sketch[j] if x!=0]\n if sum(x in ss for x in sk)>=2: continue\n sd.add(int(dochash[j])); ss.update(sk)\n sel.append(j); tot+=int(lens[j])+1\n if tot>=15e6: break\njson.dump(sel,open('cand/qgate_random.json','w')); print(\"qgate-random docs\",len(sel),\"tokens\",tot)\nEOF\nsleep 60; echo \"== submission diag:\"; cat work/run_sub.log; echo \"== official pack:\"; cat work/official_pack.log; ls -la cand/g_ml200.json cand/g_b100.json 2>&1|tail -2",
"description": "Build quality-gate-only ablation; check running jobs"
}Bash result
qgate-random docs 12201 tokens 15000876
== submission diag:
SEG 0 wiki loss 6.339 ppl 566.24
SEG 1 webprose loss 5.5705 ppl 262.57
SEG 2 news loss 5.6597 ppl 287.06
SEG 3 qa loss 5.0562 ppl 156.99
HELDOUT_PPL 286.379 loss 5.6573 train_s 51 /tmp/tr_sub.npy
== official pack:
Token indices sequence length is longer than the specified maximum sequence length for this model (1348 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 13822 ids -> /tmp/official.npy
ls: cannot access 'cand/g_b100.json': No such file or directory
-rw-r--r-- 1 root root 117257 Aug 4 01:38 cand/g_ml200.json
[stdout]
qgate-random docs 12201 tokens 15000876
== submission diag:
SEG 0 wiki loss 6.339 ppl 566.24
SEG 1 webprose loss 5.5705 ppl 262.57
SEG 2 news loss 5.6597 ppl 287.06
SEG 3 qa loss 5.0562 ppl 156.99
HELDOUT_PPL 286.379 loss 5.6573 train_s 51 /tmp/tr_sub.npy
== official pack:
Token indices sequence length is longer than the specified maximum sequence length for this model (1348 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 13822 ids -> /tmp/official.npy
ls: cannot access 'cand/g_b100.json': No such file or directory
-rw-r--r-- 1 root root 117257 Aug 4 01:38 cand/g_ml200.json
[metadata] {"noOutputExpected": false}#45
narration
The submission's p_t smoothing cost 7 ppl (286.4 vs 279.3) — uniform smoothing puts ~8% of target mass on tokens the target never uses, so greedy chases noise. The principled fix is smoothing toward the pool background:
toolBashAdd background-smoothing option to greedy
args
{
"command": "cd /workspace; python - <<'PY'\ns=open('work/greedy.py').read()\ns=s.replace('ap.add_argument(\"--artquota\",type=float,default=0.0)',\n 'ap.add_argument(\"--artquota\",type=float,default=0.0)\\nap.add_argument(\"--bgsm\",type=float,default=-1.0)')\ns=s.replace(\"\"\" c=np.bincount(dev[k*Q4:(k+1)*Q4],minlength=V).astype(np.float64)\n p_t+=TW[k]*(c+1.0)/(c.sum()+V) # register-weighted target unigram\"\"\",\n\"\"\" c=np.bincount(dev[k*Q4:(k+1)*Q4],minlength=V).astype(np.float64)\n if a.bgsm>=0: # smooth toward the achievable pool background, not uniform\n p_t+=TW[k]*(c+a.bgsm*p_bg)/(c.sum()+a.bgsm)\n else:\n p_t+=TW[k]*(c+1.0)/(c.sum()+V) # register-weighted target unigram\"\"\")\n# p_bg must exist before p_t construction\ns=s.replace(\"dev=np.load(\\\"data/multi_dev.npy\\\").astype(np.int64)\",\"dev=np.load(\\\"data/multi_dev.npy\\\").astype(np.int64)\")\nopen('work/greedy.py','w').write(s); print(\"ok\", 'bgsm' in s)\nPY\ngrep -n \"p_bg=np.load\\|TW=np.array\\|bgsm\" work/greedy.py | head",
"description": "Add background-smoothing option to greedy"
}Bash result
ok True
11:ap.add_argument("--bgsm",type=float,default=-1.0)
17:p_bg=np.load("work/p_bg.npy"); V=50257
19:TW=np.array([float(x) for x in a.tweights.split(",")]); TW/=TW.sum()
24: if a.bgsm>=0: # smooth toward the achievable pool background, not uniform
25: p_t+=TW[k]*(c+a.bgsm*p_bg)/(c.sum()+a.bgsm)
[stdout]
ok True
11:ap.add_argument("--bgsm",type=float,default=-1.0)
17:p_bg=np.load("work/p_bg.npy"); V=50257
19:TW=np.array([float(x) for x in a.tweights.split(",")]); TW/=TW.sum()
24: if a.bgsm>=0: # smooth toward the achievable pool background, not uniform
25: p_t+=TW[k]*(c+a.bgsm*p_bg)/(c.sum()+a.bgsm)
[metadata] {"noOutputExpected": false}#46
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashLaunch bg-smooth build, ablation + minlen200 runs; verify packer parity
args
{
"command": "cd /workspace; nohup python work/greedy.py --out cand/g_bg1k.json --clip 100 --bgsm 1000 > work/g_bg1k.log 2>&1 &\npython work/mypack.py cand/qgate_random.json /tmp/tr_qg.npy >/dev/null\nnohup python diag_train.py --train_npy /tmp/tr_qg.npy --heldout_npy data/multi_dev.npy --out_json /tmp/qg.json > work/run_qg.log 2>&1 &\npython work/mypack.py cand/g_ml200.json /tmp/tr_ml200.npy >/dev/null\nnohup python train_nano.py --train_npy /tmp/tr_ml200.npy --heldout_npy data/multi_dev.npy --out_json /tmp/ml200.json > work/run_ml200.log 2>&1 &\npython -c \"\nimport numpy as np\na=np.load('/tmp/official.npy'); b=np.load('/tmp/tr_sub.npy')\nprint('official packer parity:', a.shape==b.shape and bool((a==b).all()))\"\ncat .timer/remaining_secs",
"description": "Launch bg-smooth build, ablation + minlen200 runs; verify packer parity"
}Bash result
official packer parity: True
3618
[stdout]
official packer parity: True
3618
[metadata] {"noOutputExpected": false}#47
narration
Packer parity confirmed — my fast evals are byte-identical to the official pipeline. Waiting on the ablation:
toolBashCollect ablation results
args
{
"command": "cd /workspace; sleep 115; echo \"== qgate-random ablation:\"; cat work/run_qg.log; echo \"== minlen200:\"; cat work/run_ml200.log; echo \"== bg1k build:\"; tail -2 work/g_bg1k.log; cat .timer/remaining_secs",
"description": "Collect ablation results"
}Bash result
== qgate-random ablation:
SEG 0 wiki loss 6.724 ppl 832.18
SEG 1 webprose loss 5.6272 ppl 277.88
SEG 2 news loss 5.8702 ppl 354.33
SEG 3 qa loss 5.7769 ppl 322.76
HELDOUT_PPL 403.625 loss 6.0005 train_s 108 /tmp/tr_qg.npy
== minlen200:
HELDOUT_PPL 273.157 loss 5.6100 train_s 108 /tmp/tr_ml200.npy
== bg1k build:
proxy H(p_t,p_sel) = 7.861199568088065
selected 14209 docs 15008915 tokens 60.7
3498
[stdout]
== qgate-random ablation:
SEG 0 wiki loss 6.724 ppl 832.18
SEG 1 webprose loss 5.6272 ppl 277.88
SEG 2 news loss 5.8702 ppl 354.33
SEG 3 qa loss 5.7769 ppl 322.76
HELDOUT_PPL 403.625 loss 6.0005 train_s 108 /tmp/tr_qg.npy
== minlen200:
HELDOUT_PPL 273.157 loss 5.6100 train_s 108 /tmp/tr_ml200.npy
== bg1k build:
proxy H(p_t,p_sel) = 7.861199568088065
selected 14209 docs 15008915 tokens 60.7
3498
[metadata] {"noOutputExpected": false}#48
narration
Decisive ablation: quality-gate + dedup alone gives 403.6, while distribution matching takes it to 273 — the matching does the heavy lifting, not the junk filter. Also minlen 200 beats 300 (273.2 vs 279.3). Testing the smoothing fix and lower minlen:
toolBashLaunch bg-smooth eval and build combined variants
args
{
"command": "cd /workspace; python work/mypack.py cand/g_bg1k.json /tmp/tr_bg1k.npy >/dev/null\nnohup python train_nano.py --train_npy /tmp/tr_bg1k.npy --heldout_npy data/multi_dev.npy --out_json /tmp/bg1k.json > work/run_bg1k.log 2>&1 &\nnohup python work/greedy.py --out cand/g_bg150.json --clip 100 --bgsm 1000 --minlen 150 > work/g_bg150.log 2>&1 &\nnohup python work/greedy.py --out cand/g_u100.json --clip 100 --minlen 100 > work/g_u100.log 2>&1 &\necho launched; cat .timer/remaining_secs",
"description": "Launch bg-smooth eval and build combined variants"
}Bash result
launched
3478
[stdout]
launched
3478
[metadata] {"noOutputExpected": false}#49
reasoning (redacted)
encrypted chain-of-thought — recorded, not exposed
toolBashLaunch combined variant runs
args
{
"command": "cd /workspace; for f in g_bg150 g_u100; do while [ ! -f cand/$f.json ]; do sleep 4; done; done\nfor f in g_bg150 g_u100; do python work/mypack.py cand/$f.json /tmp/tr_$f.npy >/dev/null; nohup python train_nano.py --train_npy /tmp/tr_$f.npy --heldout_npy data/multi_dev.npy --out_json /tmp/$f.json > work/run_$f.log 2>&1 & done\ngrep -h proxy work/g_bg150.log work/g_u100.log; cat work/run_bg1k.log; cat .timer/remaining_secs",
"description": "Launch combined variant runs"
}Bash result
proxy H(p_t,p_sel) = 7.854294751212874
proxy H(p_t,p_sel) = 8.630015763205593
HELDOUT_PPL 272.900 loss 5.6091 train_s 51 /tmp/tr_bg1k.npy
3388
[stdout]
proxy H(p_t,p_sel) = 7.854294751212874
proxy H(p_t,p_sel) = 8.630015763205593
HELDOUT_PPL 272.900 loss 5.6091 train_s 51 /tmp/tr_bg1k.npy
3388
[metadata] {"noOutputExpected": false}#50
narration
bg-smoothing confirmed: 272.9 at minlen 300. Writing up claim.md while the combined variants train:
toolBashCollect combined variant results
args
{
"command": "cd /workspace; sleep 105; echo \"== bgsm+minlen150:\"; cat work/run_g_bg150.log; echo \"== uniform+minlen100:\"; cat work/run_g_u100.log; cat .timer/remaining_secs",
"description": "Collect combined variant results"
}Bash result
== bgsm+minlen150:
HELDOUT_PPL 267.721 loss 5.5899 train_s 107 /tmp/tr_g_bg150.npy
== uniform+minlen100:
HELDOUT_PPL 278.768 loss 5.6304 train_s 107 /tmp/tr_g_u100.npy
3267
[stdout]
== bgsm+minlen150:
HELDOUT_PPL 267.721 loss 5.5899 train_s 107 /tmp/tr_g_bg150.npy
== uniform+minlen100:
HELDOUT_PPL 278.768 loss 5.6304 train_s 107 /tmp/tr_g_u100.npy
3267
[metadata] {"noOutputExpected": false}