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nano-gpt-data-curation

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
261
duration
1h 13m
recorded
2026-08-04

run iddclm-nano-curation__XegPhjw

Everything gpt-5.6-sol did on this task, in order.

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codexgpt-5.6-sol276 turns15 narration261 reasoning261 action261 observation1 final
#51
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "32",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.7 seconds
Output:
{"chunk_id":"a32a79","wall_time_seconds":30.001948746,"session_id":17463,"original_token_count":0,"output":""}
#52
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":17463,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 33
Wall time 11.0 seconds
Output:
#53
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "33",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.0 seconds
Output:
{"chunk_id":"be29b7","wall_time_seconds":30.001723309,"session_id":17463,"original_token_count":0,"output":""}
#54
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":17463,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 34
Wall time 11.0 seconds
Output:
#55
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "34",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.4 seconds
Output:
{"chunk_id":"67bc40","wall_time_seconds":30.000743745,"session_id":17463,"original_token_count":0,"output":""}
#56
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":17463,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 35
Wall time 11.0 seconds
Output:
#57
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "35",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 12.8 seconds
Output:
{"chunk_id":"31fe5a","wall_time_seconds":30.00212885,"session_id":17463,"original_token_count":0,"output":""}
#58
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":17463,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script completed
Wall time 6.4 seconds
Output:
{"chunk_id":"61f854","wall_time_seconds":6.349527912,"exit_code":0,"original_token_count":32,"output":"ids 12387 chars 80002927 estimated mix {'encyclopedic': 22207171, 'technical': 3116318, 'news': 6562256, 'general': 48117182}\r\n"}
#59
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"python3 - <<'PY'\\nimport json\\ns=json.load(open('submission/selection.json')); w=set(s[:25]);d={}\\nfor l in open('data/pool.jsonl'):\\n r=json.loads(l)\\n if r['id'] in w:d[r['id']]=r['text']\\nfor i in s[:20]: print(i,len(d[i]),repr(d[i][:180].replace('\\\\n',' ')))\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output)\n"
}
exec result
Script completed
Wall time 3.8 seconds
Output:
85285 14636 '<|endoftext|>OPENING STATEMENT: I really feel like we got better today. As you well know, the draft is a process, and throughout the process what you try to do is add good football'
101225 16264 '�s interview, published by The London Times and Bild-Zeitung, showed him once again the classic deal maker, playing his cards very close to his chest – a point by the way he made h'
101659 32555 " piece.<|endoftext|>ALEX WOODSON: Welcome to Global Ethics Weekly. I'm Alex Woodson from Carnegie Council in New York City. This week's podcast is with Brenda Leong, senior counsel"
27547 13876 '<|endoftext|>I realized that a lot of people from the bf2s community, want to make their own signatures, but have no clue how to use Photoshop. - What will I learn from this? - Wha'
59981 26891 'Let’s talk about application. What does this look like for you and for me. The first is this principle of, you might write the word in, accountability. It’s a concept of judgment. '
56098 20797 '.<|endoftext|>Kev Rowland - Feedback Magazine One evening I was lucky enough to go over to Guy’s studio, where we spent a very pleasant evening discussing how he got involved with '
99420 23645 '<|endoftext|>Los Angeles Times The John Wooden Pyramid of Successby Neville L. Johnson The John Wooden Pyramid of Success features the words and values of the master: the official '
109782 16912 ' Tom Yorton of The Second City comedy theater argue that improvisational comedy and business have more in common than one might first think. In their new book, Yes, And: How Improv'
57677 18070 ' a massive fan of indie titles, the past week has been an exciting time to own a Nintendo Switch. For those who don’t know, Nintendo has released two videos showcasing indie games '
70591 27957 ' to everyone for coming to my sessions and the organizers for making the event run so well. The facility was great and it’s really quite remarkable that the community can have such'
32860 16281 'Laying proper legal groundwork might not be the sexiest aspect of a startup venture. But a failure to adequately address legal issues has real potential to negatively affect the va'
76137 13333 ' is one of the “Cities” series, a group of about 15 paintings that includes cities in a variety of stories and ideas about our species and how we relate to each other and our envir'
18635 15298 'Sum: What events led you to America? RDM: I left England and Cambridge University came to the U.S. in 1952 for a graduate fellowships and assistantship at Stanford. That was in the'
106182 26164 " Secrets Of Money - When Money Is Corrupted In this video, Mike Maloney talks about his travel to Berlin and Frankfurt where the world's largest Central Banks were situated. He als"
12169 34835 'I’m here today with my friend and longtime acquaintance, Susan Piver. We worked together years ago in the music industry. And she became an author. She’s evolved in many, many dire'
78811 24382 "Compiled by: Jack Foley Q. It's been recorded that during the course of making this movie you had a bit of fun playing practical jokes on one of your co-stars, Miss Streisand? A. S"
63921 30980 "<|endoftext|>Updated 21 April 22 So what's changed for entrepreneurs in 2020? A lot right? Well... perhaps not. Last week, Nova's CGO Olivia Greenberg caught up with tech talks to "
109873 22638 '10.20 MB | 25:36 Min Dr. Mark Landon is a specialist in maternal-fetal medicine at Ohio State University Medical Center who authored a landmark study and vaginal births after cesar'
62132 31714 'raham Martínez is a cinematographer who is the Director of Photography on TV shows such as Queen of the South and The Chi. We had a wonderful, in-depth conversation about his life’'
110277 14822 ' drama series Being Human, currently in its second season, follows three roommates – vampire Aidan (Sam Witwer), werewolf Josh (Sam Huntington) and ghost Sally (Meaghan Rath) – who'
#60
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"python3 pack_selection.py submission/selection.json /tmp/cand2.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 38
Wall time 11.0 seconds
Output:
#61
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "38",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.4 seconds
Output:
{"chunk_id":"cabba6","wall_time_seconds":30.001706299,"session_id":85919,"original_token_count":46,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (3450 > 1024). Running this sequence through the model will result in indexing errors\n"}
#62
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":85919,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script completed
Wall time 5.9 seconds
Output:
{"chunk_id":"a86ef3","wall_time_seconds":5.878345312,"exit_code":0,"original_token_count":14,"output":"packed 12000000 tokens from 12387 ids -> /tmp/cand2.npy\n"}
#63
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"python3 train_nano.py --train_npy /tmp/cand2.npy --heldout_npy data/multi_dev.npy --out_json /tmp/cand2.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 40
Wall time 11.0 seconds
Output:
#64
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "40",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.2 seconds
Output:
{"chunk_id":"cc74de","wall_time_seconds":30.001771524,"session_id":64121,"original_token_count":0,"output":""}
#65
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":64121,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 41
Wall time 11.0 seconds
Output:
#66
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "41",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 14.2 seconds
Output:
{"chunk_id":"ecf255","wall_time_seconds":27.752022815,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 601.735  loss 6.3998  train_s 56  /tmp/cand2.npy\n"}
#67
narration

The global-quality ablation was decisively worse (601.7), despite cleaner-looking individual pages. That confirms the main mechanism is coverage: generic polished prose undertrains the news and especially technical target blocks. I’m restoring balanced consumption, while tightening genre assignment so interviews are not mistaken for encyclopedia and incidental words like “said” do not make a personal blog into news.

#68
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-        if tech > 0.72: domain='technical'; affinity=min(1.5,tech)*.32\\n-        elif news > 0.62: domain='news'; affinity=min(1.5,news)*.22\\n-        elif enc > 0.70: domain='encyclopedic'; affinity=min(1.5,enc)*.20\\n+        tech_hits=sum(counts for word,counts in ((w, r['text'].lower().split().count(w)) for w in ())) if False else 0\\n+        # Raw hit counts guard against assigning a long page from one incidental term.\\n+        lowtext=r['text'].lower(); words_low=WORD.findall(lowtext)\\n+        tech_raw=sum(w in TECH for w in words_low)\\n+        news_raw=sum(w in NEWS for w in words_low)\\n+        fp_raw=sum(w in ('i','me','my','we','our') for w in words_low)\\n+        if tech > 0.72 and tech_raw >= 4: domain='technical'; affinity=min(1.5,tech)*.32\\n+        elif news > 0.62 and news_raw >= 5: domain='news'; affinity=min(1.5,news)*.22\\n+        elif enc > 0.70 and fp_raw/max(1,len(words_low)) < .004: domain='encyclopedic'; affinity=min(1.5,enc)*.20\\n@@\\n-    # Global quality order is primary. Register affinity is only a bounded tie\\n-    # breaker: weak automatic genre labels must not force low-quality pages into\\n-    # an artificial quota. Keep a large surplus beyond the packer's cutoff.\\n-    ranked.sort(reverse=True)\\n-    order=[]; total=0; mix=collections.Counter()\\n-    for score,i,chars,domain in ranked:\\n-        order.append(i); total += chars; mix[domain] += chars\\n-        if total >= 80_000_000: break\\n+    # Take equal character surpluses, then weave one document from each register.\\n+    # Similar top-document lengths make every prefix close to balanced, and the\\n+    # 72M-character surplus safely exceeds the 12M-token packing budget.\\n+    quota=18_000_000; by={}; mix=collections.Counter()\\n+    for domain,b in buckets.items():\\n+        by[domain]=[]; total=0\\n+        for score,i,chars in b:\\n+            by[domain].append(i); total += chars; mix[domain] += chars\\n+            if total >= quota: break\\n+    order=[]; pos={k:0 for k in by}\\n+    while True:\\n+        moved=False\\n+        for k in ('encyclopedic','general','news','technical'):\\n+            if pos[k] < len(by[k]): order.append(by[k][pos[k]]); pos[k]+=1; moved=True\\n+        if not moved: break\\n+    total=sum(mix.values())\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
#69
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-        tech_hits=sum(counts for word,counts in ((w, r['text'].lower().split().count(w)) for w in ())) if False else 0\\n         # Raw hit counts guard against assigning a long page from one incidental term.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
#70
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"cat .timer/remaining_secs; python3 -u submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":2000,\"tty\":true}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 44
Wall time 11.0 seconds
Output:
#71
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "44",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.1 seconds
Output:
{"chunk_id":"4f370d","wall_time_seconds":30.001160953,"session_id":58395,"original_token_count":2,"output":"4428\r\n"}
#72
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":58395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 45
Wall time 11.0 seconds
Output:
#73
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "45",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 15.9 seconds
Output:
{"chunk_id":"49acb3","wall_time_seconds":30.001329724,"session_id":58395,"original_token_count":0,"output":""}
#74
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":58395,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 46
Wall time 11.0 seconds
Output:
#75
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "46",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}
wait result
Script completed
Wall time 16.9 seconds
Output:
{"chunk_id":"5d38a2","wall_time_seconds":30.002064984,"session_id":58395,"original_token_count":0,"output":""}