Skip to content
RSI Bench
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
264
duration
1h 16m
recorded
2026-08-03

run iddclm-nano-curation__NAGsecC

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

download JSON ↗
codexgpt-5.6-sol295 turns31 narration264 reasoning264 action264 observation1 final
#76
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":36001,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 41
Wall time 11.0 seconds
Output:
#77
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "41",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 4.5 seconds
Output:
{"chunk_id":"a373b1","wall_time_seconds":25.456229206,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 346.466  loss 5.8478  train_s 54  /tmp/cand1.npy\n"}
#78
narration

Candidate 1 reaches dev perplexity 346.47. I’m establishing the random-selection anchor next; without that, the absolute number cannot tell us whether balancing helped or whether the forced register classifier is overfitting superficial markers.

#79
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport json,random\\nr=random.Random(1337); x=list(range(182016));r.shuffle(x);json.dump(x[:30000],open('/tmp/randomsel.json','w'))\\nPY\\npython pack_selection.py /tmp/randomsel.json /tmp/random.npy\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 42
Wall time 11.0 seconds
Output:
#80
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "42",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 16.3 seconds
Output:
{"chunk_id":"c46863","wall_time_seconds":30.001832552,"session_id":5210,"original_token_count":46,"output":"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\n"}
#81
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":5210,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script completed
Wall time 3.6 seconds
Output:
{"chunk_id":"6187ee","wall_time_seconds":3.539704929,"exit_code":0,"original_token_count":15,"output":"packed 12000000 tokens from 30000 ids -> /tmp/random.npy\n"}
#82
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
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/random.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 44
Wall time 11.0 seconds
Output:
#83
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "44",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 10.0 seconds
Output:
{"chunk_id":"806088","wall_time_seconds":30.002211523,"session_id":90658,"original_token_count":0,"output":""}
#84
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":90658,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 45
Wall time 11.0 seconds
Output:
#85
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "45",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 12.5 seconds
Output:
{"chunk_id":"ea2f88","wall_time_seconds":26.341819635,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 476.714  loss 6.1669  train_s 54  /tmp/random.npy\n"}
#86
narration

Random selection scores 476.71, so the current curation improves loss by 0.319 nats (about 27% lower perplexity). I’m now measuring the candidate’s four dev quarters separately to find which register is limiting the aggregate, then I’ll adjust only that mixture component.

#87
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"cp train_nano.py /tmp/train_domains.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output)\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
#88
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /tmp/train_domains.py\\n@@\\n print(f\\\"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}\\\")\\n+\\n+# Diagnostic only: the disclosed dev array is four equal contiguous domains.\\n+names = [\\\"wiki\\\", \\\"web\\\", \\\"news\\\", \\\"stack\\\"]\\n+for di, name in enumerate(names):\\n+    z = ho[di*250000:(di+1)*250000]\\n+    nw = (len(z)-1)//a.block; sl = 0.0; nn = 0\\n+    with torch.no_grad():\\n+      for j in range(0,nw,a.batch):\\n+        ss=list(range(j,min(j+a.batch,nw)))\\n+        x=torch.stack([z[s*a.block:s*a.block+a.block] for s in ss]).to(dev)\\n+        y=torch.stack([z[s*a.block+1:s*a.block+1+a.block] for s in ss]).to(dev)\\n+        with torch.autocast(\\\"cuda\\\",dtype=torch.bfloat16): _,ll=model(x,y)\\n+        sl += ll.item()*len(ss); nn += len(ss)\\n+    ml=sl/nn\\n+    print(f\\\"DOMAIN {name} ppl {math.exp(ml):.3f} loss {ml:.4f}\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
#89
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"python /tmp/train_domains.py --train_npy /tmp/cand1.npy --heldout_npy data/multi_dev.npy --out_json /tmp/cand1d.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script completed
Wall time 2.0 seconds
Output:
{"chunk_id":"79ecd9","wall_time_seconds":1.757177582,"exit_code":1,"original_token_count":43,"output":"Traceback (most recent call last):\n  File \"/tmp/train_domains.py\", line 4, in <module>\n    from model import GPT, GPTConfig\nModuleNotFoundError: No module named 'model'\n"}
#90
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/workspace python /tmp/train_domains.py --train_npy /tmp/cand1.npy --heldout_npy data/multi_dev.npy --out_json /tmp/cand1d.json --max_iters 3000 --seed 1337\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 49
Wall time 11.0 seconds
Output:
#91
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "49",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 11.7 seconds
Output:
{"chunk_id":"f857b1","wall_time_seconds":30.001657141,"session_id":83852,"original_token_count":0,"output":""}
#92
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":83852,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 50
Wall time 11.0 seconds
Output:
#93
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "50",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 8.4 seconds
Output:
{"chunk_id":"e95dec","wall_time_seconds":24.669391532,"exit_code":0,"original_token_count":52,"output":"HELDOUT_PPL 346.466  loss 5.8478  train_s 54  /tmp/cand1.npy\nDOMAIN wiki ppl 756.686 loss 6.6289\nDOMAIN web ppl 263.824 loss 5.5753\nDOMAIN news ppl 339.667 loss 5.8280\nDOMAIN stack ppl 211.854 loss 5.3559\n"}
#94
narration

The domain breakdown identifies Wikipedia as the bottleneck (PPL 756.7 versus 212–340 elsewhere). The hand-written encyclopedia lexicon is too narrow and exhausts its bucket. I’m replacing register assignment with a reproducible log-odds classifier learned from word distributions in the four disclosed dev quarters, while retaining the independent prose-quality score and deduplication. This uses target style, not hidden labels or IDs.

#95
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 import argparse, collections, hashlib, json, math, os, re\\n from pathlib import Path\\n+import numpy as np\\n+from transformers import AutoTokenizer\\n@@\\n-def features(o):\\n+def target_weights(path):\\n+    \\\"\\\"\\\"Smoothed one-vs-rest word log odds for each disclosed register.\\\"\\\"\\\"\\n+    tok=AutoTokenizer.from_pretrained('gpt2',local_files_only=True)\\n+    x=np.load(path); names=('encyclopedic','web','news','technical'); cs=[]\\n+    for j in range(4):\\n+      cs.append(collections.Counter(w.lower() for w in WORD.findall(tok.decode(x[j*250000:(j+1)*250000]))))\\n+    vocab=set().union(*cs); totals=[sum(c.values()) for c in cs]; V=len(vocab); out={}\\n+    for j,name in enumerate(names):\\n+      rest=sum(totals)-totals[j]\\n+      out[name]={w:max(-2.5,min(2.5,math.log((cs[j][w]+.5)/(totals[j]+.5*V))-math.log((sum(cs[k][w] for k in range(4) if k!=j)+.5)/(rest+.5*V)))) for w in vocab if sum(c[w] for c in cs)>=3}\\n+    return out\\n+\\n+def features(o, tw):\\n@@\\n-    return q, {'technical':tech,'news':news,'encyclopedic':enc}, max(2,int(len(raw)/4.05)+1), fp\\n+    manual={'technical':tech,'news':news,'encyclopedic':enc,'web':0.0}\\n+    # Target log odds are averaged rather than summed, preventing length bias.\\n+    ds={d:manual[d]*.18 + 11.0*sum(counts[w]*wt for w,wt in tw[d].items() if w in counts)/n for d in tw}\\n+    return q, ds, max(2,int(len(raw)/4.05)+1), fp\\n@@\\n-    ap=argparse.ArgumentParser(); ap.add_argument('--pool',default='/workspace/data/pool.jsonl'); ap.add_argument('--out',default='/workspace/submission/selection.json'); ap.add_argument('--report',default='/workspace/submission/selection_report.json'); ap.add_argument('--budget',type=int,default=12_000_000); a=ap.parse_args()\\n+    ap=argparse.ArgumentParser(); ap.add_argument('--pool',default='/workspace/data/pool.jsonl'); ap.add_argument('--target',default='/workspace/data/multi_dev.npy'); ap.add_argument('--out',default='/workspace/submission/selection.json'); ap.add_argument('--report',default='/workspace/submission/selection_report.json'); ap.add_argument('--budget',type=int,default=12_000_000); a=ap.parse_args()\\n+    tw=target_weights(a.target)\\n@@\\n-        o=json.loads(line); z=features(o)\\n+        o=json.loads(line); z=features(o,tw)\\n@@\\n-      best=max(r['d'],key=r['d'].get); strength=r['d'][best]\\n-      if (best=='technical' and strength>=1.35) or (best=='news' and strength>=1.12) or (best=='encyclopedic' and strength>=.92): dom=best\\n-      else: dom='web'\\n+      dom=max(r['d'],key=r['d'].get); strength=r['d'][dom]\\n@@\\n-      r['score']=r['q'] + (0 if dom=='web' else .42*min(3.0,strength))\\n+      r['score']=r['q'] + .32*max(-2.0,min(3.0,strength))\\n@@\\n-    target=a.budget*1.18\\n+    target=a.budget*1.50\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
#96
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-    ds={d:manual[d]*.18 + 11.0*sum(counts[w]*wt for w,wt in tw[d].items() if w in counts)/n for d in tw}\\n+    ds={d:manual[d]*.18 + 11.0*sum(c*tw[d].get(w,0.0) for w,c in counts.items())/n for d in tw}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
#97
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({\"cmd\":\"cp submission/selection.json /tmp/cand1_base_selection.json; python submission/curate.py\",\"workdir\":\"/workspace\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 53
Wall time 11.0 seconds
Output:
#98
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "53",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 16.3 seconds
Output:
{"chunk_id":"e469b8","wall_time_seconds":30.001103973,"session_id":94514,"original_token_count":0,"output":""}
#99
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({\"session_id\":94514,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(JSON.stringify(r))\n"
}
exec result
Script running with cell ID 54
Wall time 11.0 seconds
Output:
#100
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "54",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 15.3 seconds
Output:
{"chunk_id":"1197f1","wall_time_seconds":30.001471445,"session_id":94514,"original_token_count":0,"output":""}