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
- 115
- duration
- 43m
- recorded
- 2026-08-03
run iddclm-nano-curation__u7ZtWPZ
Everything gpt-5.6-sol did on this task, in order.
download JSON ↗encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "32",
"yield_time_ms": 60000,
"max_tokens": 3000
}wait result
Script completed
Wall time 36.3 seconds
Output:
{"chunk_id":"81b8b1","wall_time_seconds":54.777978942,"exit_code":0,"original_token_count":84,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (1101 > 1024). Running this sequence through the model will result in indexing errors\n16538 12002475 Counter({'web': 5492617, 'news': 3582746, 'ency': 1573166, 'tech': 1353946}) {'ency': 0.131, 'web': 0.458, 'news': 0.299, 'tech': 0.113}\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "let{output,...rest}=await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,random\\nids=[]\\nfor l in open('data/pool.jsonl'): ids.append(json.loads(l)['id'])\\nrandom.Random(1337).shuffle(ids)\\nopen('/tmp/random.json','w').write(json.dumps(ids))\\nPY\\npython pack_selection.py /tmp/random.json /tmp/random.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(rest);text(output)\n"
}exec result
Script running with cell ID 33
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "33",
"yield_time_ms": 60000,
"max_tokens": 2000
}wait result
Script completed
Wall time 16.7 seconds
Output:
{"chunk_id":"8c3c71","wall_time_seconds":30.002080271,"session_id":99689,"original_token_count":46}Token indices sequence length is longer than the specified maximum sequence length for this model (2126 > 1024). Running this sequence through the model will result in indexing errors
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:99689,chars:\"\",yield_time_ms:30000,max_output_tokens:2000}); text(JSON.stringify(r))\n"
}exec result
Script completed
Wall time 10.7 seconds
Output:
{"chunk_id":"84a7d0","wall_time_seconds":10.631951909,"exit_code":0,"original_token_count":15,"output":"packed 12000000 tokens from 182016 ids -> /tmp/random.npy\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.exec_command({cmd:\"python train_nano.py --train_npy /tmp/random.npy --heldout_npy data/multi_dev.npy --out_json /tmp/rr.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"883e8f","wall_time_seconds":1.002046999,"session_id":13390,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:13390,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 36
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "36",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 40.6 seconds
Output:
{"chunk_id":"f77813","wall_time_seconds":53.955312454,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 476.714 loss 6.1669 train_s 55 /tmp/random.npy\n"}The full dev run exposed an important falsification: the first heuristic scored 512.9 perplexity versus 476.7 for random. Its “lexical richness” reward was selecting uncommon vocabulary that a 30M model cannot amortize under 12M tokens. I’m discarding that candidate and revising the observable: reward target-like function-word rate and sentence structure, while treating vocabulary extremes as a penalty rather than a benefit.
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n- q=(2.5*min(uniq,.62) - 4.0*abs(sw-.43) - .018*abs(mean-22)\\n- + .16*math.log1p(nw) - 2.2*rep - 18*weird - .13*nav - .08*urls)\\n+ # High lexical diversity turned out to over-select obscure one-off terms.\\n+ # For a small model/data budget, prefer the central statistics of edited\\n+ # English and penalize *both* templated repetition and vocabulary extremes.\\n+ q=(-4.0*abs(uniq-.45) - 5.0*abs(sw-.43) - .020*abs(mean-22)\\n+ + .06*min(math.log1p(nw),8) - 2.2*rep - 18*weird - .13*nav - .08*urls)\\n*** End Patch\"; text(await tools.apply_patch(patch));\n"
}exec result
Script completed
Wall time 0.0 seconds
Output:
{}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.exec_command({cmd:\"python -u submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"43ae47","wall_time_seconds":1.000808165,"session_id":24383,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:24383,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 39
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "39",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 45.6 seconds
Output:
{"chunk_id":"546cbf","wall_time_seconds":60.001038006,"session_id":24383,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:24383,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 40
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "40",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 45.4 seconds
Output:
{"chunk_id":"994220","wall_time_seconds":60.00243083,"session_id":24383,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:24383,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 41
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "41",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 46.5 seconds
Output:
{"chunk_id":"371170","wall_time_seconds":60.001651255,"session_id":24383,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:24383,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 42
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "42",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 11.6 seconds
Output:
{"chunk_id":"e7794d","wall_time_seconds":26.076853384,"exit_code":0,"original_token_count":18,"output":"{'ency': 1948, 'web': 85570, 'news': 4845, 'tech': 2199} selected 14282\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.exec_command({cmd:\"python pack_selection.py submission/selection.json /tmp/train2.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"dfe238","wall_time_seconds":1.000856525,"session_id":16100,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:16100,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 44
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "44",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 0.0 seconds
Output:
{"chunk_id":"bd347a","wall_time_seconds":33.273397298,"exit_code":0,"original_token_count":61,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (1101 > 1024). Running this sequence through the model will result in indexing errors\npacked 12000000 tokens from 14282 ids -> /tmp/train2.npy\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.exec_command({cmd:\"python train_nano.py --train_npy /tmp/train2.npy --heldout_npy data/multi_dev.npy --out_json /tmp/r2.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r))\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"2d1d2a","wall_time_seconds":1.002655392,"session_id":96765,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r=await tools.write_stdin({session_id:96765,chars:\"\",yield_time_ms:60000,max_output_tokens:1000}); text(JSON.stringify(r))\n"
}exec result
Script running with cell ID 46
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
args
{
"cell_id": "46",
"yield_time_ms": 60000,
"max_tokens": 1000
}wait result
Script completed
Wall time 41.8 seconds
Output:
{"chunk_id":"30f312","wall_time_seconds":55.731754968,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 495.955 loss 6.2065 train_s 54 /tmp/train2.npy\n"}The revised extreme rank improved to 496.0 but still trails random. That confirms the main failure is over-concentration, not just the richness term. The next candidate keeps the crawl’s natural domain coverage and applies only conservative rejection filters; accepted documents are ordered by a stable content hash, which should improve cleanliness without shrinking topical entropy.