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: 233
duration: 1h 13m
recorded: 2026-08-04

run iddclm-nano-curation__aTQyfVE

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

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codexgpt-5.6-sol243 turns10 narration233 reasoning233 action233 observation1 final

#26

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:31797,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 18
Wall time 11.0 seconds
Output:

#27

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "18",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 14.2 seconds
Output:
{"chunk_id":"ae748b","wall_time_seconds":27.814387698,"exit_code":0,"original_token_count":38,"output":"HELDOUT_PPL 476.714  loss 6.1669  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"}

#28

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({\n  cmd: \"python3 - <<'PY'\\nimport json,random\\nrandom.seed(1337);x=list(range(110000));random.shuffle(x);json.dump(x[:30000],open('/tmp/lowrandom.json','w'))\\nPY\\nbash /workspace/dev_eval.sh /tmp/lowrandom.json\",\n  workdir: \"/workspace\",\n  yield_time_ms: 30000,\n  max_output_tokens: 3000\n});\ntext(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 19
Wall time 11.0 seconds
Output:

#29

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "19",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.7 seconds
Output:
{"chunk_id":"88ed89","wall_time_seconds":30.001846401,"session_id":47050,"original_token_count":46,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (1452 > 1024). Running this sequence through the model will result in indexing errors\n"}

#30

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:47050,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 20
Wall time 11.0 seconds
Output:

#31

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "20",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 15.5 seconds
Output:
{"chunk_id":"3a26ee","wall_time_seconds":30.002391328,"session_id":47050,"original_token_count":15,"output":"packed 12000000 tokens from 30000 ids -> /tmp/dev_train.npy\n"}

#32

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:47050,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 21
Wall time 11.0 seconds
Output:

#33

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "21",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.4 seconds
Output:
{"chunk_id":"3402f9","wall_time_seconds":30.002339791,"session_id":47050,"original_token_count":0,"output":""}

#34

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:47050,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script completed
Wall time 0.5 seconds
Output:
{"chunk_id":"5d689a","wall_time_seconds":0.448075621,"exit_code":0,"original_token_count":38,"output":"HELDOUT_PPL 474.537  loss 6.1623  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"}

#35

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /tmp/score_pool.py\\n+import json, math, re, time\\n+from collections import Counter\\n+\\n+import numpy as np\\n+from transformers import AutoTokenizer\\n+\\n+POOL = \\\"/workspace/data/pool.jsonl\\\"\\n+DEV = \\\"/workspace/data/multi_dev.npy\\\"\\n+\\n+tok = AutoTokenizer.from_pretrained(\\\"gpt2\\\")\\n+dev = np.load(DEV).astype(np.int64)\\n+V = 50257\\n+domain_counts = np.stack([\\n+    np.bincount(dev[i * 250_000:(i + 1) * 250_000], minlength=V)\\n+    for i in range(4)\\n+])\\n+\\n+texts, ids = [], []\\n+basic = []\\n+boiler_terms = (\\n+    \\\"cookie policy\\\", \\\"privacy policy\\\", \\\"terms of service\\\", \\\"all rights reserved\\\",\\n+    \\\"skip to content\\\", \\\"toggle navigation\\\", \\\"sign up\\\", \\\"log in\\\", \\\"shopping cart\\\",\\n+    \\\"javascript is required\\\", \\\"enable javascript\\\", \\\"click here\\\", \\\"contact us\\\",\\n+    \\\"related posts\\\", \\\"share this\\\", \\\"subscribe to\\\", \\\"free shipping\\\", \\\"add to cart\\\",\\n+)\\n+spam_terms = (\\n+    \\\"payday loan\\\", \\\"casino\\\", \\\"viagra\\\", \\\"coupon code\\\", \\\"promo code\\\", \\\"buy now\\\",\\n+    \\\"best price\\\", \\\"order now\\\", \\\"weight loss\\\", \\\"essay writing service\\\",\\n+)\\n+with open(POOL) as f:\\n+    for line in f:\\n+        r = json.loads(line); s = r[\\\"text\\\"]\\n+        ids.append(r[\\\"id\\\"]); texts.append(s)\\n+        n = max(1, len(s)); lo = s.lower(); lines = s.splitlines()\\n+        alpha = sum(c.isalpha() for c in s) / n\\n+        ascii_frac = sum(ord(c) < 128 for c in s) / n\\n+        words = re.findall(r\\\"[A-Za-z]+\\\", s)\\n+        nw = max(1, len(words))\\n+        common = sum(w.lower() in {\\\"the\\\",\\\"of\\\",\\\"and\\\",\\\"to\\\",\\\"in\\\",\\\"a\\\",\\\"is\\\",\\\"that\\\",\\\"for\\\",\\\"it\\\",\\\"on\\\",\\\"with\\\",\\\"as\\\",\\\"was\\\",\\\"are\\\",\\\"by\\\",\\\"this\\\",\\\"be\\\",\\\"from\\\",\\\"or\\\",\\\"an\\\",\\\"at\\\",\\\"which\\\",\\\"but\\\",\\\"not\\\",\\\"have\\\",\\\"has\\\",\\\"had\\\",\\\"were\\\",\\\"their\\\",\\\"they\\\"} for w in words) / nw\\n+        sentence = sum(s.count(x) for x in \\\".?!\\\") / nw\\n+        boiler = sum(lo.count(x) for x in boiler_terms)\\n+        spam = sum(lo.count(x) for x in spam_terms)\\n+        shortlines = sum(len(x.strip()) < 35 for x in lines) / max(1, len(lines))\\n+        # Repeated line fragments are a reliable menu/list/template signal.\\n+        normlines = [re.sub(r\\\"\\\\s+\\\", \\\" \\\", x.strip().lower()) for x in lines if len(x.strip()) >= 20]\\n+        rep = 1 - len(set(normlines)) / max(1, len(normlines))\\n+        basic.append((len(s), alpha, ascii_frac, common, sentence, boiler, spam,\\n+                      len(lines), shortlines, rep, lo.count(\\\"<|endoftext|>\\\")))\\n+\\n+print(\\\"loaded\\\", len(texts), \\\"docs\\\", flush=True)\\n+\\n+# Estimate the web-pool token distribution on a deterministic, source-stratified sample.\\n+bg = np.zeros(V, dtype=np.int64)\\n+sample = [texts[i] for i in range(0, len(texts), 12)]\\n+for j in range(0, len(sample), 512):\\n+    enc = tok(sample[j:j+512], add_special_tokens=False,\\n+              return_attention_mask=False)[\\\"input_ids\\\"]\\n+    for z in enc:\\n+        bg += np.bincount(z, minlength=V)\\n+    if j and j % 4096 == 0: print(\\\"background\\\", j, flush=True)\\n+\\n+alpha = 8.0\\n+lp_bg = np.log((bg + alpha) / (bg.sum() + alpha * V))\\n+lp_dom = np.log((domain_counts + alpha) /\\n+                (domain_counts.sum(axis=1, keepdims=True) + alpha * V))\\n+lp_mix = np.log((domain_counts.sum(axis=0) + alpha) /\\n+                (domain_counts.sum() + alpha * V))\\n+rel = lp_dom - lp_bg\\n+\\n+n = len(texts)\\n+ntok = np.zeros(n, dtype=np.int32)\\n+ce = np.zeros(n, dtype=np.float32)\\n+rels = np.zeros((n, 4), dtype=np.float32)\\n+uniq = np.zeros(n, dtype=np.float32)\\n+eos = tok.eos_token_id\\n+for j in range(0, n, 256):\\n+    enc = tok(texts[j:j+256], add_special_tokens=False,\\n+              return_attention_mask=False)[\\\"input_ids\\\"]\\n+    for k, z0 in enumerate(enc, j):\\n+        z = np.asarray(z0, dtype=np.int64)\\n+        ntok[k] = len(z) + 1\\n+        if len(z):\\n+            ce[k] = lp_mix[z].mean()\\n+            rels[k] = rel[:, z].mean(axis=1)\\n+            # Token diversity over a capped sample; low diversity catches boilerplate/repetition.\\n+            zz = z[:4096]\\n+            uniq[k] = len(np.unique(zz)) / math.sqrt(len(zz))\\n+    if j and j % 4096 == 0: print(\\\"scored\\\", j, flush=True)\\n+\\n+np.savez_compressed('/tmp/pool_scores.npz', ids=np.asarray(ids), ntok=ntok,\\n+                    basic=np.asarray(basic, dtype=np.float32), ce=ce, rel=rels,\\n+                    uniq=uniq)\\n+print(\\\"saved\\\", int(ntok.sum()), \\\"tokens\\\", flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

exec result

Script completed
Wall time 0.0 seconds
Output:
{}

#36

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.exec_command({cmd:\"ls -l /tmp/score_pool.py && python3 /tmp/score_pool.py\",workdir:\"/workspace\",yield_time_ms:30000,max_output_tokens:2000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 24
Wall time 11.0 seconds
Output:

#37

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "24",
  "yield_time_ms": 30000,
  "max_tokens": 2000
}

wait result

Script completed
Wall time 13.9 seconds
Output:
{"chunk_id":"a88d72","wall_time_seconds":30.001465417,"session_id":10795,"original_token_count":15,"output":"-rw-r--r-- 1 root root 4034 Aug  4 01:20 /tmp/score_pool.py\n"}

#38

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 25
Wall time 11.0 seconds
Output:

#39

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "25",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 15.6 seconds
Output:
{"chunk_id":"798336","wall_time_seconds":30.00144995,"session_id":10795,"original_token_count":0,"output":""}

#40

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 26
Wall time 11.0 seconds
Output:

#41

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "26",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.6 seconds
Output:
{"chunk_id":"81d8b5","wall_time_seconds":30.001536606,"session_id":10795,"original_token_count":0,"output":""}

#42

narration

The target-relative scorer is running over the full 809 MB corpus now. Its core criterion is a smoothed Moore–Lewis score: how much more probable each document’s token distribution is under each disclosed target quarter than under the raw pool, combined with independent extraction-quality and repetition features. This gives both a quality rank and a domain assignment without relying on unavailable source labels.

#43

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 27
Wall time 11.0 seconds
Output:

#44

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "27",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.1 seconds
Output:
{"chunk_id":"745171","wall_time_seconds":30.001701289,"session_id":10795,"original_token_count":0,"output":""}

#45

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 28
Wall time 11.0 seconds
Output:

#46

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "28",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.2 seconds
Output:
{"chunk_id":"601722","wall_time_seconds":30.001593395,"session_id":10795,"original_token_count":59,"output":"loaded 182016 docs\nToken indices sequence length is longer than the specified maximum sequence length for this model (2037 > 1024). Running this sequence through the model will result in indexing errors\nbackground 4096\nbackground 8192\n"}

#47

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 29
Wall time 11.0 seconds
Output:

#48

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "29",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 16.2 seconds
Output:
{"chunk_id":"78ad0c","wall_time_seconds":30.001728625,"session_id":10795,"original_token_count":20,"output":"background 12288\nscored 4096\nscored 8192\nscored 12288\nscored 16384\nscored 20480\n"}

#49

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec

args

{
  "input": "const r = await tools.write_stdin({session_id:10795,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});text(JSON.stringify(r));\n"
}

exec result

Script running with cell ID 30
Wall time 11.0 seconds
Output:

#50

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait

args

{
  "cell_id": "30",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}

wait result

Script completed
Wall time 9.1 seconds
Output:
{"chunk_id":"6fd62b","wall_time_seconds":30.001720413,"session_id":10795,"original_token_count":20,"output":"scored 24576\nscored 28672\nscored 32768\nscored 36864\nscored 40960\nscored 45056\n"}