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
- 304
- duration
- 1h 26m
- recorded
- 2026-08-03
run iddclm-nano-curation__4yt6SE3
Everything gpt-5.6-sol did on this task, in order.
download JSON ↗encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"python submission/curate.py --output /tmp/reproduced_selection.json --report /tmp/final_report.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}exec result
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Wall time 1.2 seconds
Output:
SESSION_ID=41220encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"rg -n 'near|SimHash|duplicate|cap|default' submission/curate.py claim.md\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}exec result
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claim.md:3:Under a small fixed-token budget, target-matched fluent prose with explicit token-level register balance will train a better broad English model than either random web data or a single-domain quality ranking. Long crawl pages should also be capped because a few archives, script dumps, or books otherwise consume a disproportionate share of training.
claim.md:7:The selection should have a higher rate of word bigrams observed repeatedly in the disclosed target, a stop-word and sentence-length distribution closer to that target, fewer navigation/commerce markers, no normalized exact duplicate pages, and many more distinct documents per 12M tokens than an uncapped ranking. Independently of final perplexity, exact GPT-2 accounting predicts approximately 3M consumed tokens from each of encyclopedic, web, news, and technical-Q&A registers.
claim.md:11:Hold token count, training recipe, and seed fixed. Compare this selection with random data and ablations that remove register balancing, the target-bigram term, boilerplate penalties, or the document-length cap. The hypothesis is falsified if the claimed corpus diagnostics do not change in the predicted directions, or if the curated selection fails to improve perplexity consistently on disjoint broad-domain samples (ideally across several training seeds).
claim.md:15:The method transfers to another broad English target by relearning aggregate unigram/bigram log-odds and changing the token quotas to the new target mixture; its structural noise filters remain useful for raw web data. It should not be assumed to transfer unchanged to non-English, code-dominant, dialogue-dominant, or substantially longer-context targets, where the word model, filters, length cap, and register quotas need recalibration.
submission/curate.py:11:fit to one of the four target registers. Normalized duplicate pages are suppressed.
submission/curate.py:209: ap.add_argument("--pool", type=Path, default=POOL)
submission/curate.py:210: ap.add_argument("--dev", type=Path, default=DEV)
submission/curate.py:211: ap.add_argument("--output", type=Path, default=OUT)
submission/curate.py:212: ap.add_argument("--budget", type=int, default=12_000_000)
submission/curate.py:213: ap.add_argument("--mix", default="0.25,0.25,0.25,0.25",
submission/curate.py:215: ap.add_argument("--max-chars", type=int, default=25_000,
submission/curate.py:216: help="maximum document length; use 0 to disable (default: 25000)")
submission/curate.py:217: ap.add_argument("--fluency-weight", type=float, default=7.0)
submission/curate.py:218: ap.add_argument("--report", type=Path, default=None)
submission/curate.py:241: # exact and near duplicates with four 16-bit SimHash bands.
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n- # Exact-tokenize a generous high-quality prefix of each register, filtering\\n- # exact and near duplicates with four 16-bit SimHash bands.\\n+ # Exact-tokenize a generous high-quality prefix of each register while\\n+ # suppressing pages with identical normalized word content.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}exec result
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Output:
{}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Wall time 10.0 seconds
Output:
{"chunk_id":"cdc8b2","wall_time_seconds":10.002053291,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"2e195f","wall_time_seconds":10.001263446,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"156336","wall_time_seconds":10.001005778,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"1720d2","wall_time_seconds":10.001071187,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"5f05e6","wall_time_seconds":10.00194742,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"eb1696","wall_time_seconds":10.000333819,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"6fa6b7","wall_time_seconds":10.000578448,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"82f073","wall_time_seconds":10.000643572,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"e55efe","wall_time_seconds":10.002379811,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"d1165f","wall_time_seconds":10.001500188,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"6f309b","wall_time_seconds":10.001040464,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"91d1db","wall_time_seconds":10.001089192,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"87b041","wall_time_seconds":10.000869135,"session_id":41220,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"7a217f","wall_time_seconds":10.001071217,"session_id":41220,"original_token_count":46,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (5414 > 1024). Running this sequence through the model will result in indexing errors\n"}encrypted chain-of-thought — recorded, not exposed
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{
"input": "const r = await tools.write_stdin({\"session_id\":41220,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Wall time 4.7 seconds
Output:
{"chunk_id":"96705a","wall_time_seconds":4.704409073,"exit_code":0,"original_token_count":39,"output":"{\"documents\": 12967, \"planned_tokens\": 12500021, \"tokens_by_domain\": {\"encyclopedic\": 3124558, \"web\": 3125075, \"news\": 3125003, \"technical_qa\": 3125385}}\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"cmp -s submission/selection.json /tmp/reproduced_selection.json && echo 'reproduction: byte-identical' || echo 'reproduction: MISMATCH'\\npython -m py_compile submission/curate.py\\npython - <<'PY'\\nimport json,os\\ns=json.load(open('submission/selection.json')); r=json.load(open('/tmp/final_report.json'))\\nprint('selection_ids',len(s),'unique',len(set(s)),'range',min(s),max(s))\\nprint('planned_tokens',r['planned_tokens'])\\nprint('tokens_by_domain',r['tokens_by_domain'])\\nprint('mix',r['mix'])\\nfor p in ['submission/selection.json','submission/curate.py','claim.md']:\\n print(p,os.path.getsize(p),'bytes')\\nPY\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n"
}exec result
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Wall time 0.2 seconds
Output:
reproduction: byte-identical
selection_ids 12967 unique 12967 range 5 181915
planned_tokens 12500021
tokens_by_domain {'encyclopedic': 3124558, 'web': 3125075, 'news': 3125003, 'technical_qa': 3125385}
mix {'encyclopedic': 0.25, 'web': 0.25, 'news': 0.25, 'technical_qa': 0.25}
submission/selection.json 92588 bytes
submission/curate.py 13397 bytes
claim.md 1792 bytes
915
All deliverables now validate and the default script reproduces the selection byte-for-byte. With the remaining margin, I’m running one non-destructive 30k cap interpolation around the best 25k point; the final files will change only if it beats 325.2.
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"python submission/curate.py --max-chars 30000 --output /tmp/candidate10_selection.json --report /tmp/candidate10_report.json\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\nif (r.session_id) text(`SESSION_ID=${r.session_id}`);\n"
}exec result
Script completed
Wall time 1.2 seconds
Output:
SESSION_ID=59434encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":59434,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Wall time 10.0 seconds
Output:
{"chunk_id":"17d1d5","wall_time_seconds":10.001111784,"session_id":59434,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":59434,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Output:
{"chunk_id":"1301e0","wall_time_seconds":10.001151786,"session_id":59434,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":59434,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Output:
{"chunk_id":"26bf3c","wall_time_seconds":10.002174408,"session_id":59434,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":59434,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Output:
{"chunk_id":"d3b5a5","wall_time_seconds":10.001353907,"session_id":59434,"original_token_count":0,"output":""}