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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
217
duration
52m
recorded
2026-08-04

run iddclm-nano-curation__nfvFpyv

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

download JSON ↗
codexgpt-5.6-sol227 turns10 narration217 reasoning217 action217 observation1 final
#201
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "155",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 9.4 seconds
Output:
{"chunk_id":"046cbb","wall_time_seconds":30.001503838,"session_id":92471,"original_token_count":0,"output":""}
#202
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"ps -p 5437 -o etime,pcpu,rss; du -sh /tmp/curate-* 2>/dev/null || true; cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:1000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
    ELAPSED %CPU   RSS
      03:30 95.5 4706504
171M	/tmp/curate-odd4tq1k
2927
#203
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"python -m py_compile submission/curate.py && git diff -- submission/curate.py claim.md 2>/dev/null || true\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
#204
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script running with cell ID 158
Wall time 11.0 seconds
Output:
#205
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "158",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 14.1 seconds
Output:
{"chunk_id":"9b6d9c","wall_time_seconds":30.000960772,"session_id":92471,"original_token_count":0,"output":""}
#206
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"ps -p 5437 -o etime,pcpu,rss; du -sh /tmp/curate-* 2>/dev/null || true; cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:1000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
    ELAPSED %CPU   RSS
      04:39 95.0 4713144
240M	/tmp/curate-odd4tq1k
2857
#207
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms\":30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script failed
Wall time 0.0 seconds
Output:
Script error:
SyntaxError: Invalid or unexpected token
#208
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script running with cell ID 161
Wall time 11.0 seconds
Output:
#209
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "161",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 14.1 seconds
Output:
{"chunk_id":"40daa8","wall_time_seconds":30.000637982,"session_id":92471,"original_token_count":0,"output":""}
#210
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"ps -p 5437 -o etime,pcpu,rss; du -sh /tmp/curate-* 2>/dev/null || true; cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:1000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
    ELAPSED %CPU   RSS
      05:40 94.9 4720056
303M	/tmp/curate-odd4tq1k
2797
#211
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script running with cell ID 163
Wall time 11.0 seconds
Output:
#212
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "163",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 6.2 seconds
Output:
{"chunk_id":"584882","wall_time_seconds":30.000819739,"session_id":92471,"original_token_count":0,"output":""}
#213
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"ps -p 5437 -o etime,pcpu,rss; du -sh /tmp/curate-* 2>/dev/null || true; cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:1000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
    ELAPSED %CPU   RSS
      06:26 94.9 4721972
350M	/tmp/curate-odd4tq1k
2757
#214
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script running with cell ID 165
Wall time 11.0 seconds
Output:
#215
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "165",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 5.0 seconds
Output:
{"chunk_id":"5a9762","wall_time_seconds":30.001740011,"session_id":92471,"original_token_count":0,"output":""}
#216
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:92471,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script completed
Wall time 0.0 seconds
Output:
{"chunk_id":"7cbdab","wall_time_seconds":0.00000245,"exit_code":0,"original_token_count":46,"output":"wrote 182016 unique ids to /workspace/submission/selection.json\nranked-prefix exact BPE tokens by target register: [3124702, 3125571, 3125967, 3124867]\npool documents scored: 182016\n"}
#217
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json,os,numpy as np\\nfinal=json.load(open('submission/selection.json')); best=json.load(open('/tmp/ratio30.json'))\\nprint('selection_equal_to_best', final==best)\\nprint('count',len(final),'unique',len(set(final)),'range',min(final),max(final),'first10',final[:10])\\no=np.load('/tmp/pool_offsets.npy'); total=0; used=0\\nfor i in final:\\n total+=int(o[i+1]-o[i]);used+=1\\n if total>=12_000_000:break\\nprint('exact_cached_coverage',total,'cutoff_docs',used,'reserve_bytes',sum(int(o[i+1]-o[i]) for i in final))\\nprint('artifacts',[(p,os.path.getsize(p)) for p in ['submission/selection.json','submission/curate.py','claim.md']])\\nPY\\npython -m py_compile submission/curate.py\\ncat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.3 seconds
Output:
selection_equal_to_best False
count 182016 unique 182016 range 0 182015 first10 [51268, 47889, 41186, 6138, 2579, 68884, 47959, 180237, 50793, 37522]
exact_cached_coverage 12000140 cutoff_docs 12443 reserve_bytes 200093788
artifacts [('submission/selection.json', 1345018), ('submission/curate.py', 11811), ('claim.md', 2150)]
2697
#218
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\\nA=json.load(open('submission/selection.json'));B=json.load(open('/tmp/ratio30.json'));o=np.load('/tmp/pool_offsets.npy')\\ndef ncut(x):\\n t=0\\n for n,i in enumerate(x):\\n  t+=o[i+1]-o[i]\\n  if t>=12000000:return n+1,t\\nna,ta=ncut(A);nb,tb=ncut(B)\\nprint(na,ta,nb,tb,'prefix intersection',len(set(A[:na])&set(B[:nb])),'setsame',set(A[:na])==set(B[:nb]))\\nfor i,(a,b) in enumerate(zip(A,B)):\\n if a!=b:\\n  if i<30: print(i,a,b)\\nprint('full sets',set(A)==set(B))\\nPY\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.3 seconds
Output:
12443 12000140 12443 12000140 prefix intersection 12443 setsame True
full sets True
#219
narration

The regenerated selection matches the evaluated candidate’s 12M-token document set exactly; only the within-prefix interleaving order differs because batched scoring changes floating-point tie order. Since the frozen sampler is seed-fixed, I’m running one final verification on the actual delivered ordering rather than assuming permutations are equivalent.

#220
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"bash dev_eval.sh submission/selection.json\",workdir:\"/workspace\",yield_time_ms:1000,max_output_tokens:2000});\ntext(JSON.stringify(r));\n"
}
exec result
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"4e3511","wall_time_seconds":1.001509489,"session_id":15590,"original_token_count":0,"output":""}
#221
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nfor n in ['ratio025','ratio05','ratio10','ratio20','ratio25','ratio30','ratio35','ratio40','alpha005','alpha04','alpha1','alpha5']:\\n try: print(n,json.load(open('/tmp/'+n+'.json'))[:8])\\n except:pass\\nprint('final',json.load(open('submission/selection.json'))[:8])\\nPY\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.2 seconds
Output:
ratio025 [39891, 40136, 41846, 12246, 47959, 68884, 6138, 57711]
ratio05 [66114, 40136, 47959, 6138, 41846, 68884, 12246, 37499]
ratio10 [66114, 47889, 47959, 6138, 41186, 68884, 9544, 40136]
ratio20 [2579, 47889, 41186, 6138, 68884, 51268, 47959, 9544]
ratio25 [51268, 47889, 41186, 6138, 2579, 68884, 47959, 116119]
ratio30 [51268, 47889, 41186, 6138, 2579, 68884, 47959, 180237]
ratio35 [51268, 47889, 41186, 6138, 2579, 66305, 7641, 47959]
ratio40 [51268, 47889, 41186, 6138, 50793, 66305, 7641, 47959]
alpha005 [51268, 47889, 41186, 180237, 2579, 68884, 47959, 98906]
alpha04 [51268, 47889, 41186, 6138, 2579, 66305, 68884, 47959]
alpha1 [51268, 47889, 41186, 6138, 178386, 66305, 4591, 47959]
alpha5 [178386, 46897, 41186, 6138, 66305, 47959, 26993, 2505]
final [51268, 47889, 41186, 6138, 2579, 68884, 47959, 180237]
#222
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\nA=json.load(open('submission/selection.json'));B=json.load(open('/tmp/ratio30.json'))\\nmis=[i for i,(a,b) in enumerate(zip(A,B)) if a!=b];print(len(mis),mis[:20],mis[-5:]);\\nfor i in mis[:5]:print(i,A[i],B[i])\\nPY\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:1000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.1 seconds
Output:
58 [25713, 25714, 40317, 40318, 44257, 44258, 45170, 45171, 45675, 45676, 53507, 53508, 55625, 55626, 58086, 58087, 62661, 62662, 75726, 75727] [149783, 151975, 151976, 151977, 151978]
25713 32534 55417
25714 55417 32534
40317 12446 58191
40318 58191 12446
44257 23506 87195
#223
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.write_stdin({session_id:15590,chars:\"\",yield_time_ms:30000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n"
}
exec result
Script running with cell ID 172
Wall time 11.0 seconds
Output:
#224
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolwait
args
{
  "cell_id": "172",
  "yield_time_ms": 30000,
  "max_tokens": 3000
}
wait result
Script completed
Wall time 6.8 seconds
Output:
{"chunk_id":"5e6837","wall_time_seconds":30.000704258,"session_id":15590,"original_token_count":62,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (1323 > 1024). Running this sequence through the model will result in indexing errors\npacked 12000000 tokens from 182016 ids -> /tmp/dev_train.npy\n"}
#225
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolexec
args
{
  "input": "const r = await tools.exec_command({cmd:\"cmp -s /tmp/dev_train.npy /tmp/ratio30.npy; echo packed_array_equal=$?; ps -eo pid,etime,cmd | grep train_nano | grep -v grep; cat .timer/remaining_secs\",workdir:\"/workspace\",yield_time_ms:10000,max_output_tokens:2000});\ntext(r.output);\n"
}
exec result
Script completed
Wall time 0.2 seconds
Output:
Warning: truncated output (original token count: 2301)
Total output lines: 5

packed_array_equal=0
  243       46:28 node /usr/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model azure_ai/gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -c model_reasoning_effort=xhigh -c request_max_retries=12 -c stream_max_retries=12 -c stream_idle_timeout_ms=600000 -- # Task: Curate a raw web corpus to train the best small language model  ## Context You are assembling the pretraining data for a small language model. You have a large pool of **raw web documents** at `/workspace/data/pool.jsonl` — one JSON object per line: `{"id": <int>, "text": "..."}`. The documents are unlabeled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, …301 tokens truncated…beled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, unreachable in practice). Aim to get your held-out perplexity well below the random baseline.  ## Constraints - `selection.json` must be produced by `curate.py` from a stated criterion; an   opaque hand-picked id list fails the publishability check even if it scores well. - A selection with duplicate or out-of-range ids, or too few tokens to fill the   budget, is marked invalid (no score recorded) and excluded from ranking, so it   can't win and doesn't distort averages. 
 6332       00:53 python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /tmp/dev.json --max_iters 3000 --seed 1337
2577