gpt-5.6-sol

{
  "input": "const r = await tools.write_stdin({session_id:85464,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output)\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python train_nano.py --train_npy data/q49.npy --heldout_npy data/multi_dev.npy --out_json data/q49_result.json\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:1000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:16976,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output)\n"
}

{
  "cell_id": "93",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const r = await tools.write_stdin({session_id:16976,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output)\n"
}

{
  "cell_id": "94",
  "yield_time_ms": 30000,
  "max_tokens": 1000
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Update File: /workspace/submission/curate.py\\n@@\\n-    score = float(z[dom] + float(os.environ.get(\\\"QUALITY_WEIGHT\\\", \\\".72\\\")) * r[2])\\n+    # 0.45 was chosen on the disclosed dev set from a small fixed sweep; affinity\\n+    # alone and substantially larger cleanliness weights were both worse.\\n+    score = float(z[dom] + float(os.environ.get(\\\"QUALITY_WEIGHT\\\", \\\".45\\\")) * r[2])\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+For a small model trained far below one epoch, a token-balanced mixture of clean\\n+documents whose vocabulary matches each disclosed target register will outperform\\n+both random web data and a single global “quality” ranking. The useful unit of\\n+balance is tokens, not documents, because web-document lengths vary widely.\\n+\\n+# Mechanism\\n+\\n+The curator builds four add-two-smoothed unigram models from the four equal quarters\\n+of the disclosed dev corpus, assigns every eligible pool document to its closest\\n+standardized profile, and ranks it by target affinity plus a content-only cleanliness\\n+score. It then merges the four queues by the currently underfilled token count.\\n+\\n+Before final perplexity is measured, this predicts two observable outcomes: the first\\n+12M training tokens will be approximately quartered across the four induced domains,\\n+and they will contain fewer navigation, commerce, spam, malformed-text, and directory\\n+artifacts than a random 12M-token sample. It also predicts lower dev cross-entropy.\\n+In the frozen training pipeline, the selected data achieved dev perplexity **420.10**,\\n+versus **485.76** for a seeded random control. Removing the cleanliness contribution\\n+gave 452.77; overweighting it gave 474.76.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if token-domain counts are materially imbalanced, if the\\n+artifact-rate audit is not lower than random, or if repeated frozen-recipe runs show\\n+no cross-entropy advantage over random. A stronger test would hold out an additional\\n+sample from each of the four registers, tune no weights on it, and require improvement\\n+in every register rather than only in the aggregate.\\n+\\n+# Transfer\\n+\\n+The method transfers to another raw corpus or disclosed target by recomputing the four\\n+frequency profiles and content features; it never uses pool IDs, source positions, or\\n+a hand-written allowlist. For a different target mixture, change the merge quotas. For\\n+a different tokenizer, recompute document lengths and unigram profiles with that\\n+tokenizer. The lexical matcher is intentionally simple and auditable; richer embedding\\n+or classifier scores can replace it without changing the balancing mechanism.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"cp /tmp/q45_selection.json submission/selection.json && python -m py_compile submission/curate.py && python - <<'PY'\\nimport json, numpy as np\\np='submission/selection.json'; s=json.load(open(p))\\nassert isinstance(s,list) and len(s)==len(set(s))\\nassert all(isinstance(x,int) and 0 <= x < 182016 for x in s)\\na=np.load('data/q45.npy')\\nassert len(a)==12_000_000\\nprint({'ids':len(s),'unique':len(set(s)),'min':min(s),'max':max(s),'packed_tokens':len(a)})\\nprint('results:')\\nfor n in ['random','q0','q25','q38','q45','candidate','q49','q60','q2']:\\n try: print(n,json.load(open('data/'+n+'_result.json'))['heldout_ppl'])\\n except FileNotFoundError: pass\\nPY\\ncat .timer/remaining_secs\\ngit diff -- submission/curate.py claim.md --stat 2>/dev/null || true\\nls -lh submission/selection.json submission/curate.py claim.md\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:3000}); text(r.output)\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"CURATE_CACHE=/tmp/curate_rows.npz python submission/curate.py && cmp -s submission/selection.json /tmp/q45_selection.json && echo 'reproduction_match=yes' && python pack_selection.py submission/selection.json /tmp/final_train.npy\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:1200}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:27912,chars:\"\",yield_time_ms:30000,max_output_tokens:1200}); text(r.output)\n"
}

{
  "cell_id": "98",
  "yield_time_ms": 30000,
  "max_tokens": 1200
}