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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
285
duration
1h 22m
recorded
2026-08-04

run iddclm-nano-curation__NqS7udZ

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

download JSON ↗
codexgpt-5.6-sol294 turns9 narration285 reasoning285 action285 observation1 final
#276
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "176",
  "yield_time_ms": 30000,
  "max_tokens": 10000
}
wait result
Script completed
Wall time 14.2 seconds
Output:
{"chunk_id":"432ef9","wall_time_seconds":30.000793145,"session_id":24167,"original_token_count":0,"output":""}
#277
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:24167,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r);\n"
}
exec result
Script completed
Wall time 6.0 seconds
Output:
{"chunk_id":"bd0d08","wall_time_seconds":6.025144456,"exit_code":0,"original_token_count":67,"output":"{'losses': [6.637083647681064, 5.6848569932531134, 5.690591593257716, 5.086383905567106], 'ppls': [762.8669569112149, 294.3757443229567, 296.06872110047505, 161.80370541495986], 'mean_loss': 5.77472903493975, 'ppl': 322.0571566575004, 'train_s': 55.007869720458984}\n"}
#278
narration

The turnover is clear: a contrast weight of 2.4 is the best tested point (about 310 diagnostic perplexity); 2.5 rebounds to 322 and 3.0 to 323. I’m freezing 2.4, updating the reproducible criterion to match, and repeating the exact-pack validity check.

#279
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/work/make_candidates.py\\n@@\\n-    q=loss[i] - 2.50*(generic[i]-6.0)\\n+    q=loss[i] - 2.40*(generic[i]-6.0)\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-     3. Rank documents by target CE minus 0.75 * generic-web CE, plus transparent\\n+  3. Rank documents by target CE minus 2.4 * generic-web CE, plus transparent\\n@@\\n-        q = float(target_ce[i] - .75 * (generic_ce[i] - 6.0))\\n+        q = float(target_ce[i] - 2.40 * (generic_ce[i] - 6.0))\\n*** End Patch\";\nconst a = await tools.apply_patch(patch);\nconst r = await tools.exec_command({cmd:\"python work/make_candidates.py\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:5000});\ntext(a); text(r);\n"
}
exec result
Script completed
Wall time 1.2 seconds
Output:
{}{"chunk_id":"d2d98b","wall_time_seconds":1.001488575,"session_id":60593,"original_token_count":0,"output":""}
#280
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:60593,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script running with cell ID 179
Wall time 11.0 seconds
Output:
#281
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "179",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 13.2 seconds
Output:
{"chunk_id":"19680b","wall_time_seconds":30.002435974,"session_id":60593,"original_token_count":0,"output":""}
#282
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:60593,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script running with cell ID 180
Wall time 11.0 seconds
Output:
#283
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "180",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 6.9 seconds
Output:
{"chunk_id":"d191a4","wall_time_seconds":22.544317437,"exit_code":0,"original_token_count":66,"output":"neural_equal 143021 [13898, 115804, 9565, 3754] [14134799, 118442805, 8033259, 5881447]\nneural_mix 143021 [13898, 115804, 9565, 3754] [14134799, 118442805, 8033259, 5881447]\nneural_wiki 143021 [13898, 115804, 9565, 3754] [14134799, 118442805, 8033259, 5881447]\n"}
#284
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"cp work/neural_equal.json submission/selection.json\\npython -m py_compile submission/curate.py\\npython - <<'PY'\\nimport json\\nx=json.load(open('submission/selection.json'))\\nassert len(x)==len(set(x)) and all(type(i) is int and 0<=i<182016 for i in x)\\nprint('valid_ids',len(x),'first10',x[:10])\\nPY\\npython pack_selection.py submission/selection.json work/final240_official.npy\\ncmp work/final240_official.npy work/contrast240.npy && echo PACKS_IDENTICAL\\nsha256sum submission/selection.json submission/curate.py claim.md work/final240_official.npy\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"be2a5e","wall_time_seconds":1.001580187,"session_id":26422,"original_token_count":25,"output":"valid_ids 143021 first10 [167447, 64710, 64023, 66490, 96937, 147430, 70383, 124774, 6422, 97467]\n"}
#285
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:26422,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script running with cell ID 182
Wall time 11.0 seconds
Output:
#286
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "182",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 0.6 seconds
Output:
{"chunk_id":"f0ff5c","wall_time_seconds":30.001527139,"session_id":26422,"original_token_count":46,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (1037 > 1024). Running this sequence through the model will result in indexing errors\n"}
#287
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:26422,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{"chunk_id":"226b1f","wall_time_seconds":0.000005236,"exit_code":0,"original_token_count":108,"output":"packed 12000000 tokens from 143021 ids -> work/final240_official.npy\nPACKS_IDENTICAL\n14d09334f95424e9f315b6aedd95cfb14ef35fa20b19b187be6d28e3aa604617  submission/selection.json\n81020dce15bab28afb74647946b05c1026d6a37ba44437ae841e436f1e84508f  submission/curate.py\n5a68a8d65cd5b4c6d5313bdd231f8ca71c2ac5484a005964819a9243581e1944  claim.md\nef59d20e05e58d63a3db76a0a9ca329e2070fa2c71e40ada44f2773ff4e02af2  work/final240_official.npy\n"}
#288
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 work/final240_official.npy --heldout_npy data/multi_dev.npy --out_json work/final240_result.json --max_iters 3000 --seed 1337\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"db1a00","wall_time_seconds":1.000861095,"session_id":79571,"original_token_count":0,"output":""}
#289
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"sed -n '1,45p' submission/curate.py && sed -n '205,250p' submission/curate.py && ls -lh submission/selection.json submission/curate.py claim.md && cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
{"chunk_id":"a5407e","wall_time_seconds":0.000109987,"exit_code":0,"original_token_count":738,"output":"#!/usr/bin/env python3\n\"\"\"Contrastive target-affinity curation for the 12M-token nano-GPT run.\n\nCriterion:\n  1. Train a small reference LM on the disclosed, equally weighted dev target.\n  2. Train an identical-capacity reference on a deterministic random web sample.\n  3. Rank documents by target CE minus 2.4 * generic-web CE, plus transparent\n     language/boilerplate/repetition penalties.\n  4. Infer the four target registers from their dev quarters and interleave the\n     four ranked queues by estimated token mass, so every prefix is balanced.\n\nThis is intentionally self-contained.  It requires the same CUDA/PyTorch and\ncached GPT-2 tokenizer available to the supplied training pipeline.\n\"\"\"\nimport collections\nimport hashlib\nimport json\nimport math\nimport random\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom transformers import GPT2TokenizerFast\n\nimport sys\nsys.path.insert(0, \"/workspace\")\nfrom model import GPT, GPTConfig\n\nROOT = Path(\"/workspace\")\nPOOL = ROOT / \"data/pool.jsonl\"\nDEV = ROOT / \"data/multi_dev.npy\"\nOUT = ROOT / \"submission/selection.json\"\nEOS = 50256\n\n\ndef load_pool():\n    texts = []\n    with POOL.open() as f:\n        for line in f:\n            row = json.loads(line)\n            assert row[\"id\"] == len(texts)\n            texts.append(row[\"text\"])\n    pos = [0] * 4\n    load = [0.0] * 4\n    output = []\n    while True:\n        available = [d for d in range(4) if pos[d] < len(queues[d])]\n        if not available:\n            break\n        domain = min(available, key=lambda d: load[d])\n        _, doc_id = queues[domain][pos[domain]]\n        pos[domain] += 1\n        output.append(doc_id)\n        load[domain] += len(texts[doc_id]) / (3.25 if domain == 3 else 4.05)\n    return output\n\n\ndef main():\n    texts = load_pool()\n    tok = GPT2TokenizerFast.from_pretrained(\"gpt2\", local_files_only=True)\n    dev_np = np.load(DEV)\n    dev = torch.from_numpy(dev_np.astype(np.int64))\n\n    target_model = train_reference(dev, 256, 48, 1800, 2026)\n    target_ce = score_documents(target_model, texts, tok, 256, 128)\n    del target_model\n    torch.cuda.empty_cache()\n\n    generic = pack_random_reference(texts, tok)\n    generic_model = train_reference(generic, 128, 64, 1400, 2027)\n    generic_ce = score_documents(generic_model, texts, tok, 128, 256)\n    del generic_model, generic\n    torch.cuda.empty_cache()\n\n    labels, confidence = infer_registers(texts, dev_np, tok)\n    selection = rank_and_balance(texts, target_ce, generic_ce,\n                                 labels, confidence)\n    OUT.parent.mkdir(parents=True, exist_ok=True)\n    OUT.write_text(json.dumps(selection))\n    print(f\"wrote {len(selection)} unique ranked ids to {OUT}\")\n\n\nif __name__ == \"__main__\":\n    main()\n-rw-r--r-- 1 root root 2.3K Aug  4 01:59 claim.md\n-rwxr-xr-x 1 root root 8.8K Aug  4 02:22 submission/curate.py\n-rw-r--r-- 1 root root 1.1M Aug  4 02:24 submission/selection.json\n904\n"}
#290
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:79571,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script running with cell ID 186
Wall time 11.0 seconds
Output:
#291
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "186",
  "yield_time_ms": 30000,
  "max_tokens": 5000
}
wait result
Script completed
Wall time 13.9 seconds
Output:
{"chunk_id":"da23bc","wall_time_seconds":30.002455681,"session_id":79571,"original_token_count":0,"output":""}
#292
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:79571,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r);\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{"chunk_id":"99a889","wall_time_seconds":0.000002918,"exit_code":0,"original_token_count":19,"output":"HELDOUT_PPL 310.448  loss 5.7380  train_s 54  work/final240_official.npy\n"}
#293
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,numpy as np\\nids=json.load(open('submission/selection.json'))[:13767];d=np.load('work/domain_scores2.npy').argmax(1);L=np.zeros(182016,np.int32)\\nfor line in open('data/pool.jsonl'):\\n r=json.loads(line);L[r['id']]=len(r['text'])\\nload=np.zeros(4)\\nfor i in ids:load[d[i]]+=L[i]/(3.25 if d[i]==3 else 4.05)\\nprint('domain_docs',np.bincount(d[ids],minlength=4).tolist())\\nprint('estimated_shares',np.round(load/load.sum(),4).tolist())\\nprint('frozen_result',open('work/final240_result.json').read())\\nPY\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:2000}); text(r);\n"
}
exec result
Script completed
Wall time 3.9 seconds
Output:
{"chunk_id":"a0d18e","wall_time_seconds":3.71431982,"exit_code":0,"original_token_count":66,"output":"domain_docs [3849, 2726, 4675, 2517]\nestimated_shares [0.2499, 0.2499, 0.2499, 0.2504]\nfrozen_result {\"train_npy\": \"work/final240_official.npy\", \"heldout_ppl\": 310.448288757143, \"mean_loss\": 5.7380173456589505, \"max_iters\": 3000, \"seed\": 1337, \"train_s\": 54.2}\n"}
#294
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const p = await tools.update_plan({explanation:\"Curated, calibrated, frozen, and independently validated the final 12M-token pack and all required deliverables.\",plan:[\n  {step:\"Inspect timer, frozen trainer, corpus structure, and dev target\",status:\"completed\"},\n  {step:\"Profile corpus and derive reproducible quality/domain signals\",status:\"completed\"},\n  {step:\"Generate candidates and calibrate with frozen training runs\",status:\"completed\"},\n  {step:\"Finalize selection, curate.py, and claim.md; validate deliverables\",status:\"completed\"}\n]}); text(p);\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{}
final summary

Completed all deliverables:

  • selection.json — 143,021 unique valid IDs; exact 12M-token coverage verified.
  • curate.py — reproducible contrastive, domain-balanced curation.
  • claim.md — hypothesis, mechanism, falsification, and transfer.

Frozen dev evaluation: 310.448 perplexity, versus 448.217 for random selection. Estimated selected-prefix domain shares are 25% each.