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.write_stdin({\"session_id\":24328,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}exec result
Script completed
Wall time 5.0 seconds
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
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":24328,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}exec result
Script completed
Wall time 5.0 seconds
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encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":24328,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":3000});\ntext(r.output);\n"
}exec result
Script completed
Wall time 5.0 seconds
Output:
packed 12000000 tokens from 10066 ids -> /tmp/dev_train.npy
encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":24328,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
Script running with cell ID 95
Wall time 11.0 seconds
Output:
encrypted chain-of-thought — recorded, not exposed
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{
"cell_id": "95",
"yield_time_ms": 30000,
"max_tokens": 3000
}wait result
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Wall time 12.4 seconds
Output:
{"chunk_id":"fb9fb3","wall_time_seconds":30.00101727,"session_id":24328,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":24328,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
Script completed
Wall time 10.9 seconds
Output:
{"chunk_id":"11c4c2","wall_time_seconds":10.900874552,"exit_code":0,"original_token_count":38,"output":"HELDOUT_PPL 349.968 loss 5.8578 train_s 55 /tmp/dev_train.npy\n(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)\n"}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"cat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n"
}exec result
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Wall time 0.1 seconds
Output:
4058
That reallocation worsened dev perplexity to 350.0, so the technical quarter itself matters more than its visual cleanliness suggested. The stronger fix is to stop assigning every document to exactly one class: a good programming tutorial can look like general web prose and was being withheld from the technical quota. I’m changing each domain to rank the whole pool by quality plus domain fit, reserving scarce technical/news material first and deduplicating globally.
encrypted chain-of-thought — recorded, not exposed
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{
"input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n- domain = max(range(4), key=lambda d: ds[d])\\n- confidence = ds[domain] - sorted(ds)[-2]\\n- final = q + 0.20 * ds[domain] + 0.08 * min(2.0, confidence)\\n # Low-English pages cannot become attractive merely through domain keywords.\\n if stop_rate < 0.14 or alpha_rate < 0.48 or sentences < 3:\\n- final -= 2.0\\n- return final, domain, n, {\\n+ q -= 2.0\\n+ domain_scores = [q + 0.35 * x for x in ds]\\n+ domain = max(range(4), key=lambda d: domain_scores[d])\\n+ return domain_scores, domain, n, {\\n@@\\n- ranked = [[] for _ in range(4)]\\n+ ranked = [[] for _ in range(4)]\\n@@\\n- score, domain, nw, diagnostics = result\\n- ranked[domain].append([score, doc_id, nw, diagnostics])\\n+ scores, domain, nw, diagnostics = result\\n+ for d, score in enumerate(scores):\\n+ ranked[d].append([score, doc_id, nw, diagnostics])\\n@@\\n- accepted = [[] for _ in range(4)]\\n+ accepted = [[] for _ in range(4)]\\n reports = {DOMAINS[d]: [] for d in range(4)}\\n- for d, rows in enumerate(ranked):\\n- exact_text = set()\\n+ exact_text = set()\\n+ accepted_ids = set()\\n+ # Reserve the most distinctive/scarce registers before broad web prose.\\n+ for d in (3, 2, 0, 1):\\n+ rows = ranked[d]\\n token_total = 0\\n for score, doc_id, nw, diagnostics in rows:\\n+ if doc_id in accepted_ids:\\n+ continue\\n norm_hash = hashlib.blake2b(\\\" \\\".join(words(texts[doc_id], 20000)).encode(), digest_size=12).digest()\\n@@\\n accepted[d].append((doc_id, ntok, score))\\n+ accepted_ids.add(doc_id)\\n exact_text.add(norm_hash)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}exec result
Script completed
Wall time 0.0 seconds
Output:
{}encrypted chain-of-thought — recorded, not exposed
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{
"input": "const r = await tools.exec_command({\"cmd\":\"python -m py_compile submission/curate.py && python submission/curate.py --output /tmp/candidate3_selection.json --report /tmp/candidate3_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=71125encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
Script completed
Wall time 10.0 seconds
Output:
{"chunk_id":"af4ac1","wall_time_seconds":10.001355437,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"fbd441","wall_time_seconds":10.001125813,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Output:
{"chunk_id":"199812","wall_time_seconds":10.001773573,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"1a0a87","wall_time_seconds":10.002387827,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"c66bc4","wall_time_seconds":10.000657675,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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Output:
{"chunk_id":"fa9465","wall_time_seconds":10.001634898,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"bf0100","wall_time_seconds":10.00088989,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"92d004","wall_time_seconds":10.001045487,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"0da5ed","wall_time_seconds":10.001981391,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"0b00fa","wall_time_seconds":10.001488918,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"chars\":\"\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(JSON.stringify(r));\n"
}exec result
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{"chunk_id":"ae3655","wall_time_seconds":10.002223522,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"5a9969","wall_time_seconds":10.001658352,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"8829f3","wall_time_seconds":10.000581431,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.write_stdin({\"session_id\":71125,\"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":"e0ca9b","wall_time_seconds":10.001401931,"session_id":71125,"original_token_count":0,"output":""}encrypted chain-of-thought — recorded, not exposed
args
{
"input": "const r = await tools.exec_command({\"cmd\":\"ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python | head; cat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n"
}exec result
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Wall time 0.1 seconds
Output:
PID ELAPSED %CPU %MEM RSS VSZ STAT CMD
2599 04:00 94.0 0.5 6170640 6786396 Rsl python submission/curate.py --output /tmp/candidate3_selection.json --report /tmp/candidate3_report.json
3798