gpt-5.6-sol

{
  "cell_id": "104",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:78867,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "105",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:78867,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "106",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,hashlib\\nx=json.load(open('submission/selection.json'));y=json.load(open('candidates/alloc31.json'))\\nprint('length',len(x),'unique',len(set(x)),'range',min(x),max(x),'exact_candidate_match',x==y)\\nfor p in ['submission/selection.json','candidates/alloc31.json']:\\n print(p,hashlib.sha256(open(p,'rb').read()).hexdigest())\\nPY\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});text(r.output);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /workspace/claim.md\\n+# Hypothesis\\n+\\n+Under a 12M-token budget, a small model benefits more from clean documents that match the lexical/register distribution of the disclosed high-quality target than from a larger or randomly sampled slice of raw web text. Because technical Q&A is a distinctive, high-entropy register, modestly oversampling it should improve broad average loss even though the evaluation target gives each register equal weight.\\n+\\n+# Mechanism\\n+\\n+`curate.py` learns smoothed word/phrase reference models from the four equal-token dev regions (Wikipedia, general web prose, news, and technical Q&A). It combines target-vs-raw-web log odds with extraction-quality features, ranks documents independently for each register, and quota-merges those rankings. The scheduled training prefix targets approximately 23% Wikipedia, 23% general web, 23% news, and 31% technical Q&A tokens; the latter compensates for the register's distinctive code and markup patterns.\\n+\\n+Observable predictions other than final perplexity:\\n+\\n+- Compared with a uniform pool sample, the selected prefix will have fewer navigation, cookie, login, commerce, spam, repeated-line, and script/HTML artifacts per token.\\n+- It will have higher target-reference lexical log odds, more sentence-bearing continuous prose, and far fewer tiny snippets or giant scraped pages.\\n+- Auditing GPT-2 token counts with the scheduler's domain assignments will show all four registers represented, with technical Q&A near 31% rather than a single-register collapse.\\n+- A domain-ablation selection, or an unbalanced global quality ranking, will increase loss most strongly on the omitted register even when its average surface-quality score is high.\\n+\\n+# Falsification\\n+\\n+The hypothesis is falsified if a seed-matched random selection, a quality-only selection without domain quotas, or a quota-preserving selection with scores shuffled within domains matches or beats this selection on a disjoint broad target. It is also falsified mechanistically if the selected prefix does not improve the artifact/reference statistics above, or if removing the technical oversampling does not worsen technical-Q&A loss while leaving the other registers approximately stable. The disclosed dev comparisons used only to choose criterion parameters must reproduce under the frozen trainer; otherwise the apparent gain is not attributable to curation.\\n+\\n+# Transfer\\n+\\n+The method should transfer to another raw corpus when a small representative target sample is available: relearn the contrastive phrase weights, domain reference models, extraction-stratum prior, and token-density calibration, then retune quotas on disjoint development data. The specific 115k crawl-stratum boundary, GPT-2 density factors, and 23/23/23/31 allocation are corpus/tokenizer/model-budget specific and should not be transferred unchanged. The general principle—contrast target-like content against raw-web artifacts, enforce broad coverage, and spend extra budget on a distinctive hard register—should transfer.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile submission/curate.py && env USE_TF=0 USE_FLAX=0 TRANSFORMERS_OFFLINE=1 python pack_selection.py submission/selection.json /tmp/final_train.npy && python - <<'PY'\\nimport json,numpy as np,os\\ns=json.load(open('submission/selection.json')); a=np.load('/tmp/final_train.npy')\\nassert isinstance(s,list) and len(s)==len(set(s))\\nassert all(type(x) is int and 0<=x<182016 for x in s)\\nassert len(a)==12_000_000 and a.dtype==np.uint16\\nprint('VALID ids=%d unique=%d packed_tokens=%d dtype=%s selection_bytes=%d' % (len(s),len(set(s)),len(a),a.dtype,os.path.getsize('submission/selection.json')))\\nPY\\nls -lh submission/selection.json submission/curate.py claim.md\\ncat .timer/remaining_secs\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000,\"tty\":true});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:17368,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "110",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nfor a,b in [('alloc30','alloc31'),('alloc31','balanced2.0'),('alloc31','wiki25')]:\\n x=json.load(open('candidates/'+a+'.json'))[:17000]; y=json.load(open('candidates/'+b+'.json'))[:17000]\\n print(a,b,'intersection',len(set(x)&set(y))/17000,'samepos',sum(i==j for i,j in zip(x,y))/17000)\\nPY\",\"workdir\":\"/workspace\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});text(r.output);\n"
}

{
  "input": "const patch = \"*** Begin Patch\\n*** Add File: /tmp/affinity_candidates.py\\n+import json,numpy as np\\n+S=np.load('/workspace/candidates/scores.npz');ids=S['ids'];chars=S['chars'];q=S['q'];p=S['p'];h=S['h'];valid=(chars>=500)&(chars<=35000)&(h>.15);base=q+.6*h+np.where(ids<115000,.18,-.10)\\n+factor=np.array([.9614,.9777,.9423,1.1186]);desired=np.array([.23,.23,.23,.31]);target=desired/factor;target/=target.sum()\\n+for dw in [1.6,1.8,2.2]:\\n+ ranks=[]\\n+ for d in range(4):\\n+  sc=base+dw*np.log(np.maximum(p[:,d],1e-5));ix=np.where(valid)[0];ranks.append(ix[np.argsort(-sc[ix],kind='stable')])\\n+ ptr=[0]*4;used=set();tot=np.zeros(4);out=[]\\n+ while tot.sum()<150e6:\\n+  d=int(np.argmin(tot/target+1e-6*np.arange(4)))\\n+  while ptr[d]<len(ranks[d]) and int(ranks[d][ptr[d]]) in used:ptr[d]+=1\\n+  j=int(ranks[d][ptr[d]]);ptr[d]+=1;used.add(j);out.append(j);tot[d]+=chars[j]\\n+ json.dump(out,open(f'/workspace/candidates/aff{dw}.json','w'));print(dw,len(out))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\nconst r=await tools.exec_command({cmd:\"python /tmp/affinity_candidates.py && env USE_TF=0 USE_FLAX=0 TRANSFORMERS_OFFLINE=1 bash dev_eval.sh candidates/aff1.8.json | tee candidates/aff1.8.eval\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000,\"tty\":true});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:56295,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "113",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:56295,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "114",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:56295,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "input": "const r = await tools.exec_command({cmd:\"env USE_TF=0 USE_FLAX=0 TRANSFORMERS_OFFLINE=1 bash dev_eval.sh candidates/aff2.2.json | tee candidates/aff2.2.eval\",\"workdir\":\"/workspace\",\"yield_time_ms\":1000,\"max_output_tokens\":3000,\"tty\":true});text(JSON.stringify(r));\n"
}

{
  "input": "const r = await tools.write_stdin({session_id:75503,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "117",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:75503,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}

{
  "cell_id": "118",
  "yield_time_ms": 30000,
  "max_tokens": 4000
}

{
  "input": "const r = await tools.write_stdin({session_id:75503,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output); text(JSON.stringify({session_id:r.session_id,exit_code:r.exit_code,wall:r.wall_time_seconds}));\n"
}