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RSI Bench
nano-gpt-data-curation

claude-opus-4-8

nano-gpt-data-curation — graded by the task's verifier against a measured baseline.

backend
modal
variant
claude claude-opus-4-8
metric
held-out multi-domain perplexity
actions
65
duration
59m
recorded
2026-08-04

run iddclm-nano-curation__SWfSgxK

Everything claude-opus-4-8 did on this task, in order.

download JSON ↗
claude-codeclaude-opus-4-865 turns41 narration65 action65 observation24 reasoning1 final
#26
narration

76 min left. Let me inspect per-register top docs and build the balanced selection.

toolBashPer-register top docs
args
{
  "command": "cd /workspace; python3 -c \"\nimport json,numpy as np,pickle\nR=np.load('/tmp/reg_scores.npz',allow_pickle=True); rids=R['ids']; S=R['S']; REG=list(R['reg'])\nQ=np.load('/tmp/qual.npz'); F=Q['F']\nP=pickle.load(open('/tmp/pool_tok.pkl','rb')); lens=P['lens']\nassert list(rids)==list(Q['ids'])==list(P['ids'])\nnw,mwl,alr,dgr,sw,fa,ul,uw=[F[:,i] for i in range(8)]\npassq=(lens>=128)&(lens<=3072)&(mwl>=3)&(mwl<=10)&(alr>=0.65)&(dgr<=0.15)&(sw>=0.12)&(fa>=0.80)&(ul>=0.40)&(uw>=0.35)\nassign=S.argmax(1)\ntext={}\nwith open('data/pool.jsonl') as f:\n    for line in f:\n        r=json.loads(line); text[r['id']]=r['text']\nfor r in range(4):\n    m=passq&(assign==r)\n    print('=====',REG[r],'count',int(m.sum()),'tokens %dM'%(lens[m].sum()/1e6),'=====')\n    idx=np.where(m)[0]; top=idx[np.argsort(-S[idx,r])][:4]\n    for k in top:\n        print(' id',rids[k],'sc %.3f len %d'%(S[k,r],lens[k]),repr(text[rids[k]][:120]))\n\"",
  "description": "Per-register top docs"
}
Bash result
===== wiki count 8969 tokens 5M =====
 id 8221 sc 0.378 len 965 'Follow-up and lochial Stephanus clamours his cutinization undressings unsold firstly. mesmeric Ted overtrusts, resep car'
 id 109084 sc 0.316 len 2826 'Consumptive Emory pubs, his Indo-Iranian abjured cuing tongue-in-cheek. synchronous Renaldo overmanning, her dating girl'
 id 113615 sc 0.244 len 1218 'a<|endoftext|>Czech Tobe alit it staleness checker unguardedly. lignifies sigillate that unsnarl hellishly? gassy Thache'
 id 88750 sc 0.226 len 162 'ggor Domm II (375BDW-51ADW) is widely considered to be the greatest ruler in the history of the Nethereigons. He reigned'
===== web count 111840 tokens 82M =====
 id 66445 sc 0.349 len 1302 'Derrol tasty snoring residing agro que es six sigma en espanol inflexible. Niccolo que es el algebra de funciones cannib'
 id 96487 sc 0.257 len 1237 '/.<|endoftext|>Townkomhs witk ‘oshn’ floor Slan locathg nhar train stations\nThe Ivy at Shrewsbury in Monmouth County, N.'
 id 8227 sc 0.238 len 1215 'Habilidades para la vida jovenes en accion\nCarmen bizet habanera piano sheetRhizocarpous awkward and Ruby teeters their '
 id 55143 sc 0.227 len 985 'rupts straight-arm that enthrall baggily? determinate Waylin decimated her refinancing and deploring gq india - october '
===== news count 10048 tokens 6M =====
 id 73993 sc 0.240 len 2170 '|PREDECESSORS AND SHORT HISTORY:\nin the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithviraj\nSinghji I of Jai'
 id 23673 sc 0.238 len 208 'Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police Punjab, Mohammad Ha'
 id 18830 sc 0.201 len 3062 'Archaeological dating methods\nFutilities must undress thitherward convened dismissively towerish tomato.\nPossessory tong'
 id 27085 sc 0.191 len 140 '<|endoftext|>News on : Jagan Mohan\nThe Atmakur Civil Judge on Tuesday sent TDP MLA Erra Shekhar to 14-day judicial custo'
===== qa count 1465 tokens 0M =====
 id 64048 sc 0.129 len 328 "<|endoftext|>Sometimes it's not enough for Publising features to be enabled.\nDeployment Manifest generated from Export-S"
 id 171174 sc 0.123 len 927 'ap\nHome<|endoftext|>named-compilezone: command not found\npkgs.loginroot.com\nHome\nPackages\nBinaries\nnamed-compilezone: co'
 id 12743 sc 0.107 len 173 "ZF-5830: Zend_Db_Table_Select doesn't allow use of $select->columns('..')\nZend_Db_Table_Select doesn't allow use of $sel"
 id 162670 sc 0.091 len 940 ' Terms of Service<|endoftext|>Docs For Class parserExampleInlineTag\nClass parserExampleInlineTag\nDescription\nDescription'

[stdout]
===== wiki count 8969 tokens 5M =====
 id 8221 sc 0.378 len 965 'Follow-up and lochial Stephanus clamours his cutinization undressings unsold firstly. mesmeric Ted overtrusts, resep car'
 id 109084 sc 0.316 len 2826 'Consumptive Emory pubs, his Indo-Iranian abjured cuing tongue-in-cheek. synchronous Renaldo overmanning, her dating girl'
 id 113615 sc 0.244 len 1218 'a<|endoftext|>Czech Tobe alit it staleness checker unguardedly. lignifies sigillate that unsnarl hellishly? gassy Thache'
 id 88750 sc 0.226 len 162 'ggor Domm II (375BDW-51ADW) is widely considered to be the greatest ruler in the history of the Nethereigons. He reigned'
===== web count 111840 tokens 82M =====
 id 66445 sc 0.349 len 1302 'Derrol tasty snoring residing agro que es six sigma en espanol inflexible. Niccolo que es el algebra de funciones cannib'
 id 96487 sc 0.257 len 1237 '/.<|endoftext|>Townkomhs witk ‘oshn’ floor Slan locathg nhar train stations\nThe Ivy at Shrewsbury in Monmouth County, N.'
 id 8227 sc 0.238 len 1215 'Habilidades para la vida jovenes en accion\nCarmen bizet habanera piano sheetRhizocarpous awkward and Ruby teeters their '
 id 55143 sc 0.227 len 985 'rupts straight-arm that enthrall baggily? determinate Waylin decimated her refinancing and deploring gq india - october '
===== news count 10048 tokens 6M =====
 id 73993 sc 0.240 len 2170 '|PREDECESSORS AND SHORT HISTORY:\nin the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithviraj\nSinghji I of Jai'
 id 23673 sc 0.238 len 208 'Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police Punjab, Mohammad Ha'
 id 18830 sc 0.201 len 3062 'Archaeological dating methods\nFutilities must undress thitherward convened dismissively towerish tomato.\nPossessory tong'
 id 27085 sc 0.191 len 140 '<|endoftext|>News on : Jagan Mohan\nThe Atmakur Civil Judge on Tuesday sent TDP MLA Erra Shekhar to 14-day judicial custo'
===== qa count 1465 tokens 0M =====
 id 64048 sc 0.129 len 328 "<|endoftext|>Sometimes it's not enough for Publising features to be enabled.\nDeployment Manifest generated from Export-S"
 id 171174 sc 0.123 len 927 'ap\nHome<|endoftext|>named-compilezone: command not found\npkgs.loginroot.com\nHome\nPackages\nBinaries\nnamed-compilezone: co'
 id 12743 sc 0.107 len 173 "ZF-5830: Zend_Db_Table_Select doesn't allow use of $select->columns('..')\nZend_Db_Table_Select doesn't allow use of $sel"
 id 162670 sc 0.091 len 940 ' Terms of Service<|endoftext|>Docs For Class parserExampleInlineTag\nClass parserExampleInlineTag\nDescription\nDescription'
[metadata] {"noOutputExpected": false}
#27
narration

Two problems surfaced: (1) SEO word-salad spam ("Follow-up and lochial Stephanus clamours...") tops the rankings — random diverse vocab matches every register; (2) the pool has almost no clean Q&A (only ~1.5k docs). Let me diagnose the spam signature (type-token ratio) and quantify.

toolBashDiagnose spam TTR
args
{
  "command": "cd /workspace; python3 -c \"\nimport json,numpy as np,pickle\nQ=np.load('/tmp/qual.npz'); F=Q['F']; qids=Q['ids']\nuw=F[:,7]; sw=F[:,4]\nidmap={int(i):k for k,i in enumerate(qids)}\nspam=[8221,66445,109084,8227,55143,18830,113615]\ngood=[58452,28825,79735,73993,76107]\nprint('SPAM docs: uniqword(TTR), stopword')\nfor i in spam: k=idmap[i]; print(i,'uw %.3f sw %.3f'%(uw[k],sw[k]))\nprint('GOOD docs:')\nfor i in good: k=idmap[i]; print(i,'uw %.3f sw %.3f'%(uw[k],sw[k]))\nprint('uw pctiles all',np.percentile(uw,[10,25,50,75,90,95,99]))\n\"",
  "description": "Diagnose spam TTR"
}
Bash result
SPAM docs: uniqword(TTR), stopword
8221 uw 0.697 sw 0.181
66445 uw 0.671 sw 0.136
109084 uw 0.602 sw 0.174
8227 uw 0.719 sw 0.145
55143 uw 0.700 sw 0.181
18830 uw 0.708 sw 0.193
113615 uw 0.644 sw 0.213
GOOD docs:
58452 uw 0.464 sw 0.338
28825 uw 0.704 sw 0.240
79735 uw 0.661 sw 0.322
73993 uw 0.351 sw 0.206
76107 uw 0.642 sw 0.358
uw pctiles all [0.41057847 0.49635038 0.58773094 0.68648648 0.7758621  0.82926828
 0.93846154]

[stdout]
SPAM docs: uniqword(TTR), stopword
8221 uw 0.697 sw 0.181
66445 uw 0.671 sw 0.136
109084 uw 0.602 sw 0.174
8227 uw 0.719 sw 0.145
55143 uw 0.700 sw 0.181
18830 uw 0.708 sw 0.193
113615 uw 0.644 sw 0.213
GOOD docs:
58452 uw 0.464 sw 0.338
28825 uw 0.704 sw 0.240
79735 uw 0.661 sw 0.322
73993 uw 0.351 sw 0.206
76107 uw 0.642 sw 0.358
uw pctiles all [0.41057847 0.49635038 0.58773094 0.68648648 0.7758621  0.82926828
 0.93846154]
[metadata] {"noOutputExpected": false}
#28
narration

Stopword fraction separates spam (0.13–0.21) from prose (0.24–0.36) reasonably. Word-salad also matches all registers at once. Let me check the cross-register signature.

toolBashCross-register spam signature
args
{
  "command": "cd /workspace; python3 -c \"\nimport numpy as np\nR=np.load('/tmp/reg_scores.npz',allow_pickle=True); rids=R['ids']; S=R['S']\nidmap={int(i):k for k,i in enumerate(rids)}\nspam=[8221,66445,109084,8227,55143,18830,113615]\ngood=[58452,28825,79735,73993,76107,41186,37522]\ndef show(lbl,ids):\n    print(lbl)\n    for i in ids:\n        k=idmap[i]; s=S[k]; print(' ',i,'scores',np.round(s,3),'min %.3f spread %.3f'%(s.min(),s.max()-s.min()))\nshow('SPAM',spam); show('GOOD',good)\n\"",
  "description": "Cross-register spam signature"
}
Bash result
SPAM
  8221 scores [0.378 0.354 0.315 0.089] min 0.089 spread 0.289
  66445 scores [0.208 0.349 0.177 0.047] min 0.047 spread 0.302
  109084 scores [ 0.316  0.248  0.225 -0.038] min -0.038 spread 0.355
  8227 scores [ 0.154  0.238  0.121 -0.05 ] min -0.050 spread 0.288
  55143 scores [ 0.226  0.227  0.145 -0.106] min -0.106 spread 0.333
  18830 scores [ 0.16   0.197  0.201 -0.023] min -0.023 spread 0.224
  113615 scores [ 0.244  0.171  0.166 -0.112] min -0.112 spread 0.357
GOOD
  58452 scores [-0.265 -0.447  0.145 -1.32 ] min -1.320 spread 1.465
  28825 scores [-0.151 -0.198 -0.016 -1.066] min -1.066 spread 1.049
  79735 scores [-0.329 -0.57  -0.045 -1.618] min -1.618 spread 1.573
  73993 scores [ 0.101 -0.024  0.24  -0.37 ] min -0.370 spread 0.610
  76107 scores [ 0.052 -0.339 -0.237 -1.145] min -1.145 spread 1.196
  41186 scores [-0.211 -0.245  0.167 -0.87 ] min -0.870 spread 1.037
  37522 scores [-0.33  -0.387  0.093 -1.122] min -1.122 spread 1.215

[stdout]
SPAM
  8221 scores [0.378 0.354 0.315 0.089] min 0.089 spread 0.289
  66445 scores [0.208 0.349 0.177 0.047] min 0.047 spread 0.302
  109084 scores [ 0.316  0.248  0.225 -0.038] min -0.038 spread 0.355
  8227 scores [ 0.154  0.238  0.121 -0.05 ] min -0.050 spread 0.288
  55143 scores [ 0.226  0.227  0.145 -0.106] min -0.106 spread 0.333
  18830 scores [ 0.16   0.197  0.201 -0.023] min -0.023 spread 0.224
  113615 scores [ 0.244  0.171  0.166 -0.112] min -0.112 spread 0.357
GOOD
  58452 scores [-0.265 -0.447  0.145 -1.32 ] min -1.320 spread 1.465
  28825 scores [-0.151 -0.198 -0.016 -1.066] min -1.066 spread 1.049
  79735 scores [-0.329 -0.57  -0.045 -1.618] min -1.618 spread 1.573
  73993 scores [ 0.101 -0.024  0.24  -0.37 ] min -0.370 spread 0.610
  76107 scores [ 0.052 -0.339 -0.237 -1.145] min -1.145 spread 1.196
  41186 scores [-0.211 -0.245  0.167 -0.87 ] min -0.870 spread 1.037
  37522 scores [-0.33  -0.387  0.093 -1.122] min -1.122 spread 1.215
[metadata] {"noOutputExpected": false}
#29
narration

Beautiful separation: genuine register-specific prose has a large cross-register score spread (1.0–1.6, high on its own register, very negative on Q&A), while word-salad spam matches everything uniformly (spread 0.22–0.36). A spread threshold cleanly kills spam. Let me build the full selection pipeline with a fast cache-based packer.

toolBashBuild balanced selection
args
{
  "command": "cd /workspace; cat > /tmp/build_sel.py <<'EOF'\nimport json,numpy as np,pickle,sys\nSPREAD=float(sys.argv[1]) if len(sys.argv)>1 else 0.45\nMODE=sys.argv[2] if len(sys.argv)>2 else 'balanced'\nR=np.load('/tmp/reg_scores.npz',allow_pickle=True); ids=R['ids']; S=R['S']; REG=list(R['reg'])\nQ=np.load('/tmp/qual.npz'); F=Q['F']\nP=pickle.load(open('/tmp/pool_tok.pkl','rb')); lens=P['lens']\nassert list(ids)==list(Q['ids'])==list(P['ids'])\nnw,mwl,alr,dgr,sw,fa,ul,uw=[F[:,i] for i in range(8)]\npassq=(lens>=128)&(lens<=3072)&(mwl>=3)&(mwl<=10)&(alr>=0.65)&(dgr<=0.15)&(sw>=0.15)&(fa>=0.80)&(ul>=0.40)&(uw>=0.35)&(uw<=0.85)\nspread=S.max(1)-S.min(1)\nkeep=passq&(spread>=SPREAD)\nassign=S.argmax(1); best=S.max(1)\nprint('passq',int(passq.sum()),'keep(anti-spam)',int(keep.sum()))\n# dedup exact by normalized prefix signature\ntext={}\nfor line in open('/workspace/data/pool.jsonl'):\n    r=json.loads(line); text[r['id']]=r['text']\nimport re\ndef sig(t):\n    t=re.sub(r'\\s+',' ',t.lower()).strip(); return hash(t[:160])\norder_by=np.argsort(-best)\nif MODE=='overall':\n    sel=[]; seen=set(); tot=0\n    for k in order_by:\n        if not keep[k]: continue\n        i=int(ids[k]); s=sig(text[i])\n        if s in seen: continue\n        seen.add(s); sel.append(i); tot+=lens[k]+1\n        if tot>=13_000_000: break\nelse:\n    CAP=3_200_000\n    perreg=[[] for _ in range(4)]; seen=set()\n    for r in range(4):\n        idx=np.where(keep&(assign==r))[0]\n        idx=idx[np.argsort(-S[idx,r])]\n        tot=0\n        for k in idx:\n            i=int(ids[k]); sg=sig(text[i])\n            if sg in seen: continue\n            seen.add(sg); perreg[r].append(i); tot+=lens[k]+1\n            if tot>=CAP: break\n        print(REG[r],'picked',len(perreg[r]),'tokens %.2fM'%(tot/1e6))\n    # round-robin interleave\n    sel=[]; ptr=[0,0,0,0]\n    while True:\n        added=False\n        for r in range(4):\n            if ptr[r]<len(perreg[r]):\n                sel.append(perreg[r][ptr[r]]); ptr[r]+=1; added=True\n        if not added: break\n    # overflow: fill from remaining best-overall to guarantee >12M even if a reg short\n    have=set(sel)\n    for k in order_by:\n        if len(sel)>0 and sum(lens[ids_i] for ids_i in [])>=0: pass\n        i=int(ids[k])\n        if not keep[k] or i in have: continue\n        sel.append(i); have.add(i)\n        # stop when overflow enough\nlenmap={int(i):int(l) for i,l in zip(ids,lens)}\ntot=np.cumsum([lenmap[i]+1 for i in sel])\nncov=int(np.searchsorted(tot,12_000_000))+1\nprint('selection size',len(sel),'first',ncov,'ids cover 12M tokens, total tokens listed %.1fM'%(tot[-1]/1e6))\njson.dump(sel, open('/workspace/submission/selection.json','w'))\nprint('wrote selection.json')\nEOF\npython3 /tmp/build_sel.py 0.45 balanced",
  "description": "Build balanced selection"
}
Bash result
passq 125524 keep(anti-spam) 88029
wiki picked 5383 tokens 3.20M
web picked 4463 tokens 3.20M
news picked 5392 tokens 3.20M
qa picked 627 tokens 0.31M
selection size 88029 first 18431 ids cover 12M tokens, total tokens listed 63.3M
wrote selection.json

[stdout]
passq 125524 keep(anti-spam) 88029
wiki picked 5383 tokens 3.20M
web picked 4463 tokens 3.20M
news picked 5392 tokens 3.20M
qa picked 627 tokens 0.31M
selection size 88029 first 18431 ids cover 12M tokens, total tokens listed 63.3M
wrote selection.json
[metadata] {"noOutputExpected": false}
#30
narration

qa is genuinely scarce (0.31M tokens). Let me verify spam is gone from the tops, then set up a fast cache-based packer and run a dev eval.

toolBashFast packer + verify tops
args
{
  "command": "cd /workspace; cat > /tmp/fastpack.py <<'EOF'\nimport json,numpy as np,pickle,sys\nSEL=sys.argv[1]; OUT=sys.argv[2]; BUDGET=12_000_000; EOS=50256\nP=pickle.load(open('/tmp/pool_tok.pkl','rb'))\ntokmap={int(i):d for i,d in zip(P['ids'],P['tokdocs'])}\nsel=json.load(open(SEL)); parts=[]; tot=0\nfor i in sel:\n    d=tokmap[i]; parts.append(d); parts.append(np.array([EOS],dtype=np.uint16)); tot+=len(d)+1\n    if tot>=BUDGET: break\narr=np.concatenate(parts)[:BUDGET].astype(np.uint16)\nnp.save(OUT,arr); print('packed',len(arr),'tokens from',SEL)\nEOF\n# verify tops\npython3 -c \"\nimport json,numpy as np\nsel=json.load(open('submission/selection.json'))\ntext={}\nfor line in open('data/pool.jsonl'):\n    r=json.loads(line); text[r['id']]=r['text']\nprint('first 8 (round-robin wiki/web/news/qa):')\nfor i in sel[:8]: print(' id',i,repr(text[i][:100]))\n\"",
  "description": "Fast packer + verify tops"
}
Bash result
first 8 (round-robin wiki/web/news/qa):
 id 88750 'ggor Domm II (375BDW-51ADW) is widely considered to be the greatest ruler in the history of the Neth'
 id 76686 ' Created by: brenna wilson\n- Created on: 26-03-13 20:16\nQuod ubi est Philodamo nuntiatum, tametsi er'
 id 73993 '|PREDECESSORS AND SHORT HISTORY:\nin the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithvi'
 id 64048 "<|endoftext|>Sometimes it's not enough for Publising features to be enabled.\nDeployment Manifest gen"
 id 94952 ' poist is on liberal actress Alyssa Milano.\nLeftist- Venus on 12th house cusp conjunct Neptune, rule'
 id 90245 '.<|endoftext|>Flashback: Latinos for Trump Co-Founder Stuns CNN Panel\nLatinos for Trump co-founder J'
 id 23673 'Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police'
 id 171174 'ap\nHome<|endoftext|>named-compilezone: command not found\npkgs.loginroot.com\nHome\nPackages\nBinaries\nn'

[stdout]
first 8 (round-robin wiki/web/news/qa):
 id 88750 'ggor Domm II (375BDW-51ADW) is widely considered to be the greatest ruler in the history of the Neth'
 id 76686 ' Created by: brenna wilson\n- Created on: 26-03-13 20:16\nQuod ubi est Philodamo nuntiatum, tametsi er'
 id 73993 '|PREDECESSORS AND SHORT HISTORY:\nin the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithvi'
 id 64048 "<|endoftext|>Sometimes it's not enough for Publising features to be enabled.\nDeployment Manifest gen"
 id 94952 ' poist is on liberal actress Alyssa Milano.\nLeftist- Venus on 12th house cusp conjunct Neptune, rule'
 id 90245 '.<|endoftext|>Flashback: Latinos for Trump Co-Founder Stuns CNN Panel\nLatinos for Trump co-founder J'
 id 23673 'Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police'
 id 171174 'ap\nHome<|endoftext|>named-compilezone: command not found\npkgs.loginroot.com\nHome\nPackages\nBinaries\nn'
[metadata] {"noOutputExpected": false}
#31
narration

Spam is gone; tops are coherent. Now let me run a real dev eval on the balanced selection, and in parallel prepare a random baseline for reference.

toolBashDev eval balanced selection
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false\npython3 /tmp/fastpack.py submission/selection.json /tmp/bal_train.npy\necho \"=== training balanced ===\"\ntime python3 train_nano.py --train_npy /tmp/bal_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/bal.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL",
  "description": "Dev eval balanced selection"
}
Bash result
packed 12000000 tokens from submission/selection.json
=== training balanced ===
HELDOUT_PPL 366.136  loss 5.9030  train_s 54  /tmp/bal_train.npy

real	0m58.952s
user	0m56.680s
sys	0m1.430s

[stdout]
packed 12000000 tokens from submission/selection.json
=== training balanced ===
HELDOUT_PPL 366.136  loss 5.9030  train_s 54  /tmp/bal_train.npy

real	0m58.952s
user	0m56.680s
sys	0m1.430s
[metadata] {"noOutputExpected": false}
#32
narration

PPL = 366. I need reference points. Let me build a random baseline and an unbalanced "top-overall-quality" variant, and eval both.

toolBashEval random baseline and unbalanced variant
args
{
  "command": "cd /workspace\n# random baseline selection (reproducible)\npython3 -c \"\nimport json,numpy as np,pickle\nP=pickle.load(open('/tmp/pool_tok.pkl','rb')); ids=list(map(int,P['ids']))\nrng=np.random.default_rng(0); rng.shuffle(ids)\njson.dump(ids[:40000], open('/tmp/rand_sel.json','w'))\nprint('random sel written')\n\"\npython3 /tmp/build_sel.py 0.45 overall && cp submission/selection.json /tmp/overall_sel.json\necho \"=== pack+train RANDOM ===\"\npython3 /tmp/fastpack.py /tmp/rand_sel.json /tmp/rand_train.npy\npython3 train_nano.py --train_npy /tmp/rand_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/r.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL\necho \"=== pack+train OVERALL(unbalanced) ===\"\npython3 /tmp/fastpack.py /tmp/overall_sel.json /tmp/ov_train.npy\npython3 train_nano.py --train_npy /tmp/ov_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/o.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL",
  "description": "Eval random baseline and unbalanced variant"
}
Bash result
Command did not complete within its 120s timeout and was moved to the background (ID: bi17mg2a6). Output is being written to: /tmp/claude-0/-workspace/dd765d82-e59e-4793-9e5b-596c62317df2/tasks/bi17mg2a6.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": "bi17mg2a6", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#33
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait for random+overall eval
args
{
  "command": "sleep 130; cat /tmp/claude-0/-workspace/dd765d82-e59e-4793-9e5b-596c62317df2/tasks/bi17mg2a6.output",
  "description": "Wait for random+overall eval"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure
#34
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashRead full bg output and json results
args
{
  "command": "cat /tmp/claude-0/-workspace/dd765d82-e59e-4793-9e5b-596c62317df2/tasks/bi17mg2a6.output; echo \"---jsons---\"; cat /tmp/r.json 2>/dev/null; echo; cat /tmp/o.json 2>/dev/null",
  "description": "Read full bg output and json results"
}
Bash result
random sel written
passq 125524 keep(anti-spam) 88029
selection size 19268 first 17909 ids cover 12M tokens, total tokens listed 13.0M
wrote selection.json
=== pack+train RANDOM ===
packed 12000000 tokens from /tmp/rand_sel.json
HELDOUT_PPL 485.420  loss 6.1850  train_s 54  /tmp/rand_train.npy
=== pack+train OVERALL(unbalanced) ===
packed 12000000 tokens from /tmp/overall_sel.json
HELDOUT_PPL 380.669  loss 5.9419  train_s 54  /tmp/ov_train.npy
---jsons---
{"train_npy": "/tmp/rand_train.npy", "heldout_ppl": 485.4196274453974, "mean_loss": 6.185013728085629, "max_iters": 3000, "seed": 1337, "train_s": 54.2}
{"train_npy": "/tmp/ov_train.npy", "heldout_ppl": 380.668641674896, "mean_loss": 5.9419292898222045, "max_iters": 3000, "seed": 1337, "train_s": 54.5}

[stdout]
random sel written
passq 125524 keep(anti-spam) 88029
selection size 19268 first 17909 ids cover 12M tokens, total tokens listed 13.0M
wrote selection.json
=== pack+train RANDOM ===
packed 12000000 tokens from /tmp/rand_sel.json
HELDOUT_PPL 485.420  loss 6.1850  train_s 54  /tmp/rand_train.npy
=== pack+train OVERALL(unbalanced) ===
packed 12000000 tokens from /tmp/overall_sel.json
HELDOUT_PPL 380.669  loss 5.9419  train_s 54  /tmp/ov_train.npy
---jsons---
{"train_npy": "/tmp/rand_train.npy", "heldout_ppl": 485.4196274453974, "mean_loss": 6.185013728085629, "max_iters": 3000, "seed": 1337, "train_s": 54.2}
{"train_npy": "/tmp/ov_train.npy", "heldout_ppl": 380.668641674896, "mean_loss": 5.9419292898222045, "max_iters": 3000, "seed": 1337, "train_s": 54.5}
[metadata] {"noOutputExpected": false}
#35
narration

Clear signal: random 485 → overall 380 → balanced 366. Curation and register-balancing both help. Let me check time budget and see where PPL is worst by evaluating per-quarter.

toolBashTime + split dev into quarters
args
{
  "command": "cat /workspace/.timer/remaining_secs; cd /workspace\npython3 -c \"\nimport numpy as np\nho=np.load('data/multi_dev.npy'); N=len(ho)\nfor r,name in enumerate(['wiki','web','news','qa']):\n    np.save('/tmp/dev_%s.npy'%name, ho[r*N//4:(r+1)*N//4])\nprint('quarters saved')\n\"",
  "description": "Time + split dev into quarters"
}
Bash result
4069
quarters saved

[stdout]
4069
quarters saved
[metadata] {"noOutputExpected": false}
#36
narration

~68 min left. Let me write a diagnostic that trains identically but reports PPL per quarter — to find where to invest.

toolBashPer-quarter PPL for balanced
args
{
  "command": "cd /workspace; cat > /tmp/diag.py <<'EOF'\nimport sys,math,numpy as np,torch\nfrom model import GPT,GPTConfig\ntrain_npy=sys.argv[1]\ntorch.manual_seed(1337); np.random.seed(1337)\ndev='cuda'; block=256; batch=32; lr=6e-4; max_iters=3000; warmup=150\ntr=torch.from_numpy(np.load(train_npy).astype(np.int64))\nrng=np.random.default_rng(1337)\nmodel=GPT(GPTConfig(block_size=block,vocab_size=50257,n_layer=6,n_head=6,n_embd=384,dropout=0.0,bias=False)).to(dev)\nopt=model.configure_optimizers(0.1,lr,(0.9,0.95),'cuda')\ndef lr_at(it):\n    if it<warmup: return lr*(it+1)/(warmup+1)\n    r=(it-warmup)/max(1,max_iters-warmup); return 0.1*lr+0.5*(1+math.cos(math.pi*r))*(lr-0.1*lr)\ndef gb():\n    ix=rng.integers(0,len(tr)-block-1,size=batch)\n    x=torch.stack([tr[i:i+block] for i in ix]).to(dev); y=torch.stack([tr[i+1:i+1+block] for i in ix]).to(dev); return x,y\nmodel.train()\nfor it in range(max_iters):\n    for g in opt.param_groups: g['lr']=lr_at(it)\n    x,y=gb()\n    with torch.autocast('cuda',dtype=torch.bfloat16): _,loss=model(x,y)\n    opt.zero_grad(set_to_none=True); loss.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(),1.0); opt.step()\nmodel.eval()\ndef ppl(ho):\n    ho=torch.from_numpy(ho.astype(np.int64)); nwin=(len(ho)-1)//block; starts=[j*block for j in range(nwin)]\n    sl,ns=0.0,0\n    with torch.no_grad():\n        for i in range(0,nwin,batch):\n            bs=starts[i:i+batch]; x=torch.stack([ho[s:s+block] for s in bs]).to(dev); y=torch.stack([ho[s+1:s+1+block] for s in bs]).to(dev)\n            with torch.autocast('cuda',dtype=torch.bfloat16): _,l=model(x,y)\n            sl+=l.item()*len(bs); ns+=len(bs)\n    return math.exp(sl/ns)\nfull=np.load('data/multi_dev.npy')\nprint('FULL %.1f'%ppl(full), end='  ')\nfor name in ['wiki','web','news','qa']:\n    print('%s %.1f'%(name,ppl(np.load('/tmp/dev_%s.npy'%name))),end='  ')\nprint()\nEOF\npython3 /tmp/diag.py /tmp/bal_train.npy 2>&1 | grep -E 'FULL|wiki'",
  "description": "Per-quarter PPL for balanced"
}
Bash result
(Bash completed with no output)

[metadata] {"returnCodeInterpretation": "No matches found", "noOutputExpected": false}
#37
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPer-quarter PPL (retry, show output)
args
{
  "command": "cd /workspace; python3 /tmp/diag.py /tmp/bal_train.npy 2>&1 | tail -3",
  "description": "Per-quarter PPL (retry, show output)"
}
Bash result
File "/tmp/diag.py", line 2, in <module>
    from model import GPT,GPTConfig
ModuleNotFoundError: No module named 'model'

[stdout]
  File "/tmp/diag.py", line 2, in <module>
    from model import GPT,GPTConfig
ModuleNotFoundError: No module named 'model'
[metadata] {"noOutputExpected": false}
#38
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashRun diag from workspace
args
{
  "command": "cd /workspace; python3 /tmp/diag.py /tmp/bal_train.npy 2>&1 | tail -3",
  "description": "Run diag from workspace"
}
Bash result
File "/tmp/diag.py", line 2, in <module>
    from model import GPT,GPTConfig
ModuleNotFoundError: No module named 'model'

[stdout]
  File "/tmp/diag.py", line 2, in <module>
    from model import GPT,GPTConfig
ModuleNotFoundError: No module named 'model'
[metadata] {"noOutputExpected": false}
#39
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashRun diag with PYTHONPATH
args
{
  "command": "PYTHONPATH=/workspace python3 /tmp/diag.py /tmp/bal_train.npy 2>&1 | tail -3",
  "description": "Run diag with PYTHONPATH"
}
Bash result
FULL 366.1  wiki 688.3  web 268.2  news 257.9  qa 376.1

[stdout]
FULL 366.1  wiki 688.3  web 268.2  news 257.9  qa 376.1
[metadata] {"noOutputExpected": false}
#40
narration

Key finding — per-quarter PPL: wiki 688 (worst), qa 376, web 268, news 258. Wikipedia is by far the hardest and most data-hungry; news is easy/saturated. Equal token allocation is suboptimal. Let me check available tokens per register and test a wiki-heavy allocation.

toolBashAvailable tokens per register
args
{
  "command": "cd /workspace; python3 -c \"\nimport numpy as np,pickle\nR=np.load('/tmp/reg_scores.npz',allow_pickle=True); ids=R['ids']; S=R['S']\nQ=np.load('/tmp/qual.npz'); F=Q['F']\nP=pickle.load(open('/tmp/pool_tok.pkl','rb')); lens=P['lens']\nnw,mwl,alr,dgr,sw,fa,ul,uw=[F[:,i] for i in range(8)]\npassq=(lens>=128)&(lens<=3072)&(mwl>=3)&(mwl<=10)&(alr>=0.65)&(dgr<=0.15)&(sw>=0.15)&(fa>=0.80)&(ul>=0.40)&(uw>=0.35)&(uw<=0.85)\nspread=S.max(1)-S.min(1); keep=passq&(spread>=0.45); assign=S.argmax(1)\nfor r,n in enumerate(['wiki','web','news','qa']):\n    m=keep&(assign==r); print(n,'docs',int(m.sum()),'tokens %.1fM'%(lens[m].sum()/1e6))\n\"",
  "description": "Available tokens per register"
}
Bash result
wiki docs 7687 tokens 4.5M
web docs 71431 tokens 53.4M
news docs 8224 tokens 5.0M
qa docs 687 tokens 0.4M

[stdout]
wiki docs 7687 tokens 4.5M
web docs 71431 tokens 53.4M
news docs 8224 tokens 5.0M
qa docs 687 tokens 0.4M
[metadata] {"noOutputExpected": false}
#41
narration

Available: wiki 4.5M (all of it), web 53M, news 5M, qa 0.4M. Since wiki drives the mean and news saturates, let me test a wiki-heavy allocation. Let me make the builder take per-register token caps.

toolBashBuild+eval wiki-heavy allocation
args
{
  "command": "cd /workspace; cat > /tmp/build2.py <<'EOF'\nimport json,numpy as np,pickle,sys,re\n# caps in Mtokens: wiki web news qa ; out path\ncw,cwb,cn,cq=[float(x)*1e6 for x in sys.argv[1:5]]\nOUT=sys.argv[5]\ncaps=[cw,cwb,cn,cq]\nR=np.load('/tmp/reg_scores.npz',allow_pickle=True); ids=R['ids']; S=R['S']\nQ=np.load('/tmp/qual.npz'); F=Q['F']\nP=pickle.load(open('/tmp/pool_tok.pkl','rb')); lens=P['lens']\nnw,mwl,alr,dgr,sw,fa,ul,uw=[F[:,i] for i in range(8)]\npassq=(lens>=128)&(lens<=3072)&(mwl>=3)&(mwl<=10)&(alr>=0.65)&(dgr<=0.15)&(sw>=0.15)&(fa>=0.80)&(ul>=0.40)&(uw>=0.35)&(uw<=0.85)\nspread=S.max(1)-S.min(1); keep=passq&(spread>=0.45); assign=S.argmax(1); best=S.max(1)\ntext={}\nfor line in open('/workspace/data/pool.jsonl'):\n    r=json.loads(line); text[r['id']]=r['text']\ndef sig(t): return hash(re.sub(r'\\s+',' ',t.lower()).strip()[:160])\nseen=set(); perreg=[[] for _ in range(4)]\nfor r in range(4):\n    idx=np.where(keep&(assign==r))[0]; idx=idx[np.argsort(-S[idx,r])]; tot=0\n    for k in idx:\n        i=int(ids[k]); sg=sig(text[i])\n        if sg in seen: continue\n        seen.add(sg); perreg[r].append((i,int(lens[k]))); tot+=lens[k]+1\n        if tot>=caps[r]: break\n# weighted round-robin by caps\nw=[max(1,round(c/1e6)) for c in caps]\nsel=[]; ptr=[0,0,0,0]\nwhile any(ptr[r]<len(perreg[r]) for r in range(4)):\n    for r in range(4):\n        for _ in range(w[r]):\n            if ptr[r]<len(perreg[r]): sel.append(perreg[r][ptr[r]][0]); ptr[r]+=1\n# overflow from best overall\nhave=set(sel)\nfor k in np.argsort(-best):\n    if not keep[k]: continue\n    i=int(ids[k])\n    if i in have: continue\n    sel.append(i); have.add(i)\n    if len(have)>len(sel)-1 and len(sel)>60000: break\njson.dump(sel,open(OUT,'w'))\nlenmap={int(i):int(l) for i,l in zip(ids,lens)}\ntot=np.cumsum([lenmap[i]+1 for i in sel]); ncov=int(np.searchsorted(tot,12_000_000))+1\n# composition of first 12M\ncomp=[0,0,0,0]; am={int(i):int(a) for i,a in zip(ids,assign)}\nfor i in sel[:ncov]: comp[am[i]]+=lenmap[i]+1\nprint(OUT,'size',len(sel),'cover12M at',ncov,'comp(M) wiki %.1f web %.1f news %.1f qa %.1f'%tuple(c/1e6 for c in comp))\nEOF\npython3 /tmp/build2.py 4.5 4.6 2.6 0.4 /tmp/sel_wikiheavy.json\nexport TOKENIZERS_PARALLELISM=false\npython3 /tmp/fastpack.py /tmp/sel_wikiheavy.json /tmp/wh_train.npy\nPYTHONPATH=/workspace python3 /tmp/diag.py /tmp/wh_train.npy 2>&1 | tail -1",
  "description": "Build+eval wiki-heavy allocation"
}
Bash result
/tmp/sel_wikiheavy.json size 60001 cover12M at 18943 comp(M) wiki 4.4 web 4.6 news 2.6 qa 0.3
packed 12000000 tokens from /tmp/sel_wikiheavy.json
FULL 356.3  wiki 674.7  web 264.8  news 255.2  qa 352.5

[stdout]
/tmp/sel_wikiheavy.json size 60001 cover12M at 18943 comp(M) wiki 4.4 web 4.6 news 2.6 qa 0.3
packed 12000000 tokens from /tmp/sel_wikiheavy.json
FULL 356.3  wiki 674.7  web 264.8  news 255.2  qa 352.5
[metadata] {"noOutputExpected": false}
#42
narration

Wiki-heavy helped: 366 → 356 (all quarters improved). Wiki (675) is still the bottleneck but data-limited. Let me sweep a few allocations in one background run to find the best split.

toolBashSweep allocations in background
args
{
  "command": "cd /workspace; cat > /tmp/sweep.py <<'EOF'\nimport subprocess,os\nos.environ['TOKENIZERS_PARALLELISM']='false'; os.environ['PYTHONPATH']='/workspace'\nallocs={\n 'A_webheavy':'4.5 5.6 1.5 0.4',\n 'B_noqa':'4.6 4.7 2.7 0.0',\n 'C_balhi':'4.5 4.0 3.1 0.4',\n 'D_wikimax_web':'4.5 5.0 2.1 0.4',\n}\nfor name,q in allocs.items():\n    sel=f'/tmp/sel_{name}.json'; tr=f'/tmp/tr_{name}.npy'\n    subprocess.run(f'python3 /tmp/build2.py {q} {sel}',shell=True,check=True)\n    subprocess.run(f'python3 /tmp/fastpack.py {sel} {tr}',shell=True,check=True)\n    r=subprocess.run(f'python3 /tmp/diag.py {tr}',shell=True,capture_output=True,text=True)\n    print(name,q,'::',r.stdout.strip().split('\\n')[-1],flush=True)\nEOF\nnohup python3 /tmp/sweep.py > /tmp/sweep.log 2>&1 &\necho started $!",
  "description": "Sweep allocations in background"
}
Bash result
started 2763

[stdout]
started 2763
[metadata] {"noOutputExpected": false}
#43
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait sweep (partial)
args
{
  "command": "sleep 300; cat /tmp/sweep.log; echo \"time:\"; cat /workspace/.timer/remaining_secs",
  "description": "Wait sweep (partial)"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure
#44
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashPoll sweep log
args
{
  "command": "sleep 110; cat /tmp/sweep.log; echo \"time:\"; cat /workspace/.timer/remaining_secs",
  "description": "Poll sweep log"
}
Bash result
/tmp/sel_A_webheavy.json size 60001 cover12M at 18623 comp(M) wiki 4.4 web 5.6 news 1.7 qa 0.3
packed 12000000 tokens from /tmp/sel_A_webheavy.json
A_webheavy 4.5 5.6 1.5 0.4 :: FULL 365.4  wiki 691.0  web 262.3  news 266.9  qa 367.0
/tmp/sel_B_noqa.json size 60001 cover12M at 18793 comp(M) wiki 4.4 web 4.7 news 2.7 qa 0.1
packed 12000000 tokens from /tmp/sel_B_noqa.json
B_noqa 4.6 4.7 2.7 0.0 :: FULL 384.8  wiki 669.8  web 269.0  news 264.6  qa 458.2
/tmp/sel_C_balhi.json size 60001 cover12M at 19100 comp(M) wiki 4.4 web 4.1 news 3.2 qa 0.3
packed 12000000 tokens from /tmp/sel_C_balhi.json
C_balhi 4.5 4.0 3.1 0.4 :: FULL 361.6  wiki 674.0  web 271.8  news 257.7  qa 360.9
/tmp/sel_D_wikimax_web.json size 60001 cover12M at 18752 comp(M) wiki 4.4 web 5.1 news 2.2 qa 0.3
packed 12000000 tokens from /tmp/sel_D_wikimax_web.json
time:
3578

[stdout]
/tmp/sel_A_webheavy.json size 60001 cover12M at 18623 comp(M) wiki 4.4 web 5.6 news 1.7 qa 0.3
packed 12000000 tokens from /tmp/sel_A_webheavy.json
A_webheavy 4.5 5.6 1.5 0.4 :: FULL 365.4  wiki 691.0  web 262.3  news 266.9  qa 367.0
/tmp/sel_B_noqa.json size 60001 cover12M at 18793 comp(M) wiki 4.4 web 4.7 news 2.7 qa 0.1
packed 12000000 tokens from /tmp/sel_B_noqa.json
B_noqa 4.6 4.7 2.7 0.0 :: FULL 384.8  wiki 669.8  web 269.0  news 264.6  qa 458.2
/tmp/sel_C_balhi.json size 60001 cover12M at 19100 comp(M) wiki 4.4 web 4.1 news 3.2 qa 0.3
packed 12000000 tokens from /tmp/sel_C_balhi.json
C_balhi 4.5 4.0 3.1 0.4 :: FULL 361.6  wiki 674.0  web 271.8  news 257.7  qa 360.9
/tmp/sel_D_wikimax_web.json size 60001 cover12M at 18752 comp(M) wiki 4.4 web 5.1 news 2.2 qa 0.3
packed 12000000 tokens from /tmp/sel_D_wikimax_web.json
time:
3578
[metadata] {"noOutputExpected": false}
#45
narration

qa is essential (removing it → 385, qa quarter 458). Best remains wiki-heavy 4.5/4.6/2.6/0.4 = 356. Let me get the D result and check time.

toolBashD result
args
{
  "command": "sleep 20; tail -2 /tmp/sweep.log",
  "description": "D result"
}
Bash result
/tmp/sel_D_wikimax_web.json size 60001 cover12M at 18752 comp(M) wiki 4.4 web 5.1 news 2.2 qa 0.3
packed 12000000 tokens from /tmp/sel_D_wikimax_web.json

[stdout]
/tmp/sel_D_wikimax_web.json size 60001 cover12M at 18752 comp(M) wiki 4.4 web 5.1 news 2.2 qa 0.3
packed 12000000 tokens from /tmp/sel_D_wikimax_web.json
[metadata] {"noOutputExpected": false}
#46
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashD final
args
{
  "command": "sleep 45; tail -1 /tmp/sweep.log",
  "description": "D final"
}
Bash result
D_wikimax_web 4.5 5.0 2.1 0.4 :: FULL 368.2  wiki 682.0  web 269.1  news 264.6  qa 377.8

[stdout]
D_wikimax_web 4.5 5.0 2.1 0.4 :: FULL 368.2  wiki 682.0  web 269.1  news 264.6  qa 377.8
[metadata] {"noOutputExpected": false}
#47
narration

D worse (368). Winner confirmed: wiki 4.5 / web 4.6 / news 2.6 / qa 0.4 = 356. Let me make one attempt to crack the wiki bottleneck by relaxing the spread filter to admit more encyclopedic data.

toolBashTest relaxed-spread wiki boost
args
{
  "command": "cd /workspace\n# add optional spread & lenmax args to build2\npython3 - <<'EOF'\ns=open('/tmp/build2.py').read()\ns=s.replace(\"OUT=sys.argv[5]\",\"OUT=sys.argv[5]\\nSPREAD=float(sys.argv[6]) if len(sys.argv)>6 else 0.45\\nLENMAX=int(sys.argv[7]) if len(sys.argv)>7 else 3072\")\ns=s.replace(\"(lens<=3072)\",\"(lens<=LENMAX)\")\ns=s.replace(\"spread>=0.45\",\"spread>=SPREAD\")\nopen('/tmp/build2.py','w').write(s); print('patched')\nEOF\npython3 /tmp/build2.py 5.5 4.0 2.1 0.4 /tmp/sel_wrelax.json 0.32 4096\nexport TOKENIZERS_PARALLELISM=false PYTHONPATH=/workspace\npython3 /tmp/fastpack.py /tmp/sel_wrelax.json /tmp/wr_train.npy\npython3 /tmp/diag.py /tmp/wr_train.npy 2>&1 | tail -1",
  "description": "Test relaxed-spread wiki boost"
}
Bash result
patched
/tmp/sel_wrelax.json size 60001 cover12M at 18082 comp(M) wiki 5.1 web 4.3 news 2.2 qa 0.4
packed 12000000 tokens from /tmp/sel_wrelax.json
FULL 357.9  wiki 652.0  web 273.7  news 271.0  qa 337.7

[stdout]
patched
/tmp/sel_wrelax.json size 60001 cover12M at 18082 comp(M) wiki 5.1 web 4.3 news 2.2 qa 0.4
packed 12000000 tokens from /tmp/sel_wrelax.json
FULL 357.9  wiki 652.0  web 273.7  news 271.0  qa 337.7
[metadata] {"noOutputExpected": false}
#48
narration

More wiki data does lower wiki PPL (675→652) but trades off web/news — net 358, just shy of 356. I'm at a plateau. Let me try one balanced-relaxed combo, then finalize.

toolBashCombined relaxed allocation
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false PYTHONPATH=/workspace\npython3 /tmp/build2.py 4.9 4.7 2.2 0.4 /tmp/sel_fin.json 0.38 3584\npython3 /tmp/fastpack.py /tmp/sel_fin.json /tmp/fin_train.npy\npython3 /tmp/diag.py /tmp/fin_train.npy 2>&1 | tail -1",
  "description": "Combined relaxed allocation"
}
Bash result
/tmp/sel_fin.json size 60001 cover12M at 18548 comp(M) wiki 4.8 web 4.7 news 2.1 qa 0.4
packed 12000000 tokens from /tmp/sel_fin.json
FULL 336.8  wiki 595.0  web 269.7  news 258.5  qa 309.1

[stdout]
/tmp/sel_fin.json size 60001 cover12M at 18548 comp(M) wiki 4.8 web 4.7 news 2.1 qa 0.4
packed 12000000 tokens from /tmp/sel_fin.json
FULL 336.8  wiki 595.0  web 269.7  news 258.5  qa 309.1
[metadata] {"noOutputExpected": false}
#49
narration

Big jump: 336.8 (wiki 595, qa 309) from spread=0.38 + longer docs. Training is deterministic (fixed seed/data), so this is real. Let me sweep around this optimum.

toolBashSweep around optimum
args
{
  "command": "cd /workspace; cat > /tmp/sweep2.py <<'EOF'\nimport subprocess,os\nos.environ['TOKENIZERS_PARALLELISM']='false'; os.environ['PYTHONPATH']='/workspace'\nruns={\n 'E':'5.2 4.4 2.0 0.4 0.38 3584',\n 'F':'4.9 4.7 2.2 0.4 0.35 4096',\n 'G':'4.9 4.7 2.2 0.4 0.40 3584',\n 'H':'5.4 4.3 1.9 0.4 0.33 4096',\n}\nfor n,q in runs.items():\n    sel=f'/tmp/s_{n}.json'; tr=f'/tmp/t_{n}.npy'\n    subprocess.run(f'python3 /tmp/build2.py {q} {sel}'.replace(q, q.split(\" 0.\")[0]+\" \"+sel+\" \"+\".\".join([\"0\"]+q.split(\" 0.\")[1].split(\".\")) if False else q,1),shell=True)\nEOF\necho \"scrap\"; # simpler: inline\ncat > /tmp/sweep2.py <<'EOF'\nimport subprocess,os\nos.environ['TOKENIZERS_PARALLELISM']='false'; os.environ['PYTHONPATH']='/workspace'\nruns={\n 'E':('5.2','4.4','2.0','0.4','0.38','3584'),\n 'F':('4.9','4.7','2.2','0.4','0.35','4096'),\n 'G':('4.9','4.7','2.2','0.4','0.40','3584'),\n 'H':('5.4','4.3','1.9','0.4','0.33','4096'),\n}\nfor n,q in runs.items():\n    sel=f'/tmp/s_{n}.json'; tr=f'/tmp/t_{n}.npy'\n    w,wb,nw_,qa,sp,lm=q\n    subprocess.run(f'python3 /tmp/build2.py {w} {wb} {nw_} {qa} {sel} {sp} {lm}',shell=True,check=True)\n    subprocess.run(f'python3 /tmp/fastpack.py {sel} {tr}',shell=True,check=True)\n    r=subprocess.run(f'python3 /tmp/diag.py {tr}',shell=True,capture_output=True,text=True)\n    print(n,q,'::',r.stdout.strip().split('\\n')[-1],flush=True)\nEOF\nnohup python3 /tmp/sweep2.py > /tmp/sweep2.log 2>&1 & echo started $!",
  "description": "Sweep around optimum"
}
Bash result
scrap
started 3744

[stdout]
scrap
started 3744
[metadata] {"noOutputExpected": false}
#50
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait sweep2
args
{
  "command": "sleep 300; cat /tmp/sweep2.log; echo \"time:\"; cat /workspace/.timer/remaining_secs",
  "description": "Wait sweep2"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure