gpt-5.6-sol
nano-gpt-data-curation — graded by the task's verifier against a measured baseline.
- backend
- modal
- variant
- codex azure_ai/gpt-5.6-sol
- metric
- held-out multi-domain perplexity
- actions
- 187
- duration
- 50m
- recorded
- 2026-08-03
run iddclm-nano-curation__XWHDKVK
Everything gpt-5.6-sol did on this task, in order.
download JSON ↗encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "114",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 15.7 seconds
Output:
Token indices sequence length is longer than the specified maximum sequence length for this model (2000 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 50000 ids -> /tmp/trainw400.npy
SESSION_ID=40589encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:40589,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 115
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "115",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 9.5 seconds
Output:
SESSION_ID=40589encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:40589,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script completed
Wall time 6.8 seconds
Output:
QUARTER 0 PPL 749.4938654851136 LOSS 6.619398132699435
QUARTER 1 PPL 268.6688180286438 LOSS 5.593479461357242
QUARTER 2 PPL 293.1571206438673 LOSS 5.680708713218814
QUARTER 3 PPL 153.23300425535425 LOSS 5.03195966658045
HELDOUT_PPL 308.648 loss 5.7322 train_s 50 /tmp/trainw400.npy
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,hashlib\\nwith open('/tmp/seghashes.jsonl','w') as f:\\n for line in open('data/pool.jsonl'):\\n d=json.loads(line);hs=[]\\n for seg in d['text'].split('<|endoftext|>'):\\n z=' '.join(seg.lower().split())\\n if len(z)>=500:hs.append(hashlib.blake2b(z.encode(),digest_size=8).hexdigest())\\n if not hs:\\n z=' '.join(d['text'].lower().split());hs=[hashlib.blake2b(z.encode(),digest_size=8).hexdigest()]\\n f.write(json.dumps([d['id'],hs])+'\\\\n')\\nPY\\npython - <<'PY'\\nimport numpy as np,json\\nhs={i:z for i,z in map(json.loads,open('/tmp/seghashes.jsonl'))};r=np.loadtxt('/tmp/scores.tsv');r=r[(r[:,1]>=900)&(r[:,1]<=30000)];lists=[]\\nfor j in range(4):\\n ds=r[:,3+j]-np.max(np.delete(r[:,3:7],j,axis=1),axis=1);sc=r[:,2]+.8*ds;o=np.argsort(-sc);lists.append([(int(r[k,0]),r[k,1]) for k in o])\\npos=[0]*4;used=set();uh=set();tot=[0.]*4;out=[];fac=[.27,.27,.27,.34];skip=0\\nwhile len(out)<50000:\\n j=min(range(4),key=lambda x:tot[x])\\n while True:\\n i,ch=lists[j][pos[j]];pos[j]+=1\\n if i not in used and not any(x in uh for x in hs[i]):break\\n skip+=1\\n used.add(i);uh.update(hs[i]);out.append(i);tot[j]+=ch*fac[j]\\njson.dump(out,open('/tmp/segdedup.json','w'));print(tot,'skips',skip)\\nPY\\npython pack_selection.py /tmp/segdedup.json /tmp/trainseg.npy\\nPYTHONPATH=/workspace python /tmp/train_diag.py --train_npy /tmp/trainseg.npy --heldout_npy data/multi_dev.npy --out_json /tmp/diagseg.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
SESSION_ID=49108encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:49108,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 118
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "118",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 15.9 seconds
Output:
[12426951.690000039, 12421353.240000002, 12421242.270000037, 12422908.10000002] skips 39039
Token indices sequence length is longer than the specified maximum sequence length for this model (2000 > 1024). Running this sequence through the model will result in indexing errors
SESSION_ID=49108encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:49108,chars:\"\",yield_time_ms\":30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script failed
Wall time 0.0 seconds
Output:
Script error:
SyntaxError: Invalid or unexpected tokenencrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:49108,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 120
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "120",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 15.7 seconds
Output:
packed 12000000 tokens from 50000 ids -> /tmp/trainseg.npy
SESSION_ID=49108encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:49108,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 121
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "121",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 9.4 seconds
Output:
QUARTER 0 PPL 754.2400534454664 LOSS 6.62571069060779
QUARTER 1 PPL 267.48845790632424 LOSS 5.589076417391418
QUARTER 2 PPL 295.3649628002279 LOSS 5.688211753720143
QUARTER 3 PPL 150.2248536106178 LOSS 5.0121331957520034
HELDOUT_PPL 307.758 loss 5.7293 train_s 51 /tmp/trainseg.npy
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({cmd:\"cmp -s /tmp/trainseg.npy /tmp/traindedup.npy; echo $?; python - <<'PY'\\nimport json\\nx=json.load(open('/tmp/segdedup.json'));y=json.load(open('/tmp/bal08dedup.json'))\\nprint(next((i for i,z in enumerate(zip(x,y)) if z[0]!=z[1]),None))\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output)\n"
}exec result
Script completed
Wall time 0.2 seconds
Output:
0
37641
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport numpy as np,json\\nh={int(a):b for a,b in map(str.split,open('/tmp/hashes.tsv'))};r=np.loadtxt('/tmp/scores.tsv');r=r[(r[:,1]>=900)&(r[:,1]<=30000)];lists=[]\\nfor j in range(4):\\n ds=r[:,3+j]-np.max(np.delete(r[:,3:7],j,axis=1),axis=1);sc=r[:,2]+.85*ds;o=np.argsort(-sc);lists.append([(int(r[k,0]),r[k,1]) for k in o])\\npos=[0]*4;used=set();uh=set();tot=[0.]*4;out=[];fac=[.27,.27,.27,.34]\\nwhile len(out)<50000:\\n j=min(range(4),key=lambda x:tot[x])\\n while True:\\n i,ch=lists[j][pos[j]];pos[j]+=1\\n if i not in used and h[i] not in uh:break\\n used.add(i);uh.add(h[i]);out.append(i);tot[j]+=ch*fac[j]\\njson.dump(out,open('/tmp/bal085.json','w'));print(tot)\\nPY\\npython pack_selection.py /tmp/bal085.json /tmp/train085.npy\\nPYTHONPATH=/workspace python /tmp/train_diag.py --train_npy /tmp/train085.npy --heldout_npy data/multi_dev.npy --out_json /tmp/diag085.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
[12441969.630000044, 12441456.089999992, 12442183.47000005, 12441855.620000053]
SESSION_ID=4821encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:4821,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 124
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "124",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 15.5 seconds
Output:
Token indices sequence length is longer than the specified maximum sequence length for this model (2000 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 50000 ids -> /tmp/train085.npy
SESSION_ID=4821encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:4821,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script running with cell ID 125
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "125",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
Script completed
Wall time 12.1 seconds
Output:
SESSION_ID=4821encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({session_id:4821,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}exec result
Script completed
Wall time 6.0 seconds
Output:
QUARTER 0 PPL 783.063672452733 LOSS 6.663214011270492
QUARTER 1 PPL 264.9980540230478 LOSS 5.5797224826500065
QUARTER 2 PPL 295.9498951114318 LOSS 5.690190166723533
QUARTER 3 PPL 155.76667008981767 LOSS 5.0483591830144166
HELDOUT_PPL 312.990 loss 5.7462 train_s 50 /tmp/train085.npy
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.update_plan({explanation:\"Final script regenerated the submitted selection; the packed 12M-token array exactly matches the best validated candidate.\",plan:[\n {step:\"Inspect corpus, frozen trainer, dev data, and time budget\",status:\"completed\"},\n {step:\"Profile document sources/quality and implement candidate rankers\",status:\"completed\"},\n {step:\"Benchmark candidates with frozen training runs\",status:\"completed\"},\n {step:\"Generate final selection and claim, then validate deliverables\",status:\"completed\"}\n]}); text(r)\n"
}exec result
Script completed
Wall time 0.0 seconds
Output:
{}Completed and validated:
- selection.json: 50,000 unique, valid IDs; packs exactly 12M tokens.
- curate.py: reproducible target-similarity, quality, balance, and deduplication pipeline.
- claim.md: required scientific claim sections.
Frozen dev perplexity: 307.758, versus 449.386 for random selection.
The deliverables were already finalized before the interruption. I’ll do one quick integrity check on the exact paths and preserve the validated selection unchanged.
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({cmd:\"cat .timer/remaining_secs && python - <<'PY'\\nimport json, pathlib, py_compile\\nroot=pathlib.Path('/workspace')\\nsel=json.load(open(root/'submission/selection.json'))\\nassert len(sel)==len(set(sel)) and min(sel)>=0 and max(sel)<182016\\npy_compile.compile(str(root/'submission/curate.py'),doraise=True)\\nassert (root/'claim.md').is_file()\\nprint({'ids':len(sel),'unique':len(set(sel)),'min':min(sel),'max':max(sel),'deliverables':'valid'})\\nPY\\nls -lh submission/selection.json submission/curate.py claim.md\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output)\n"
}exec result
Script completed
Wall time 0.2 seconds
Output:
2576
{'ids': 50000, 'unique': 50000, 'min': 1, 'max': 182015, 'deliverables': 'valid'}
-rw-r--r-- 1 root root 1.9K Aug 3 23:13 claim.md
-rw-r--r-- 1 root root 7.2K Aug 3 23:13 submission/curate.py
-rw-r--r-- 1 root root 343K Aug 3 23:19 submission/selection.json
Integrity check passed. All required deliverables are finalized at the exact requested paths; the selection contains 50,000 unique valid IDs and covers 12M tokens.