gpt-5.6-sol

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root       256  0.0  0.0  12568  5452 ?        Ss   01:11   0:00 bash -c  rm -f /tmp/ri-exec-b5427a18159f4dc3bf4768125dd764ec.pid setsid bash -c 'set -o pipefail;  export PATH="$HOME/.local/bin:/usr/local/bin:$PATH" if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi OUT=/logs/agent/codex.txt mkdir -p "$(dirname "$OUT")" : > "$OUT" ATT="$(mktemp /tmp/codex-attempt.XXXXXX)" trap '"'"'rm -f "$ATT"'"'"' EXIT  read_remaining() {   local r=""   if [ -r /workspace/.timer/remaining_secs ]; then     r="$(cat /workspace/.timer/remaining_secs 2>/dev/null)"   fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r="${TASK_BUDGET_SECS:-}"; fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r=86400; fi   echo "$r" }  MIN_REM=90 MAX_RESUMES=15 BASE=20 CAP=240 JIT=25 N=0 rc=1 while : ; do   REM="$(read_remaining)"   if [ "$REM" -le "$MIN_REM" ] 2>/dev/null; then     echo "=== codex: budget exhausted (remaining=${REM}s); stopping ===" | tee -a "$OUT"     break   fi   : > "$ATT"   if [ "$N" -eq 0 ]; then     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=high -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-…4621 tokens truncated… 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. 
root      1521  0.0  0.0  12852  6316 ?        Ss   01:20   0:00 /bin/bash -c ps aux | rg 'train_nano|dev_eval'; stat -c '%y %s' /tmp/dev.json
root      1523  0.0  0.0  17932 11052 ?        S    01:20   0:00 rg train_nano|dev_eval
2026-08-04 01:20:41.208785486 +0000 152

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{"train_npy": "/tmp/dev_train.npy", "heldout_ppl": 469.92328973766223, "mean_loss": 6.152569468059237, "max_iters": 3000, "seed": 1337, "train_s": 55.8}

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root      1553 97.3  0.5 6829580 6211752 ?     Rl   01:21   0:32 python3 -
root      1619  0.0  0.0  12852  5748 ?        Ss   01:21   0:00 /bin/bash -c ps aux | rg "python3 -" | tail -3
root      1621  0.0  0.0  17932 11476 ?        S    01:21   0:00 rg python3 -

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{"chunk_id":"11bc0c","wall_time_seconds":30.002394607,"session_id":86186,"original_token_count":176}lenblock [(16103, 522573), (10939, 126393), (7818, 119484), (10607, 117510), (2563, 116723), (297, 114469), (9500, 111544), (1912, 103709), (3515, 91820), (3052, 91361)] 1 17999
lenprior [(297, 114469), (16103, 522573), (2563, 116723), (1912, 103709), (394, 73221), (1896, 82349), (3052, 91361), (3515, 91820), (7818, 119484), (1297, 57099)] 1 56877
lenlight [(297, 114469), (394, 73221), (1912, 103709), (2563, 116723), (1896, 82349), (1297, 57099), (3052, 91361), (698, 43748), (3515, 91820), (38, 33891)] 0 25624
Token indices sequence length is longer than the specified maximum sequence length for this model (123095 > 1024). Running this sequence through the model will result in indexing errors

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root       256  0.0  0.0  12568  5452 ?        Ss   01:11   0:00 bash -c  rm -f /tmp/ri-exec-b5427a18159f4dc3bf4768125dd764ec.pid setsid bash -c 'set -o pipefail;  export PATH="$HOME/.local/bin:/usr/local/bin:$PATH" if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi OUT=/logs/agent/codex.txt mkdir -p "$(dirname "$OUT")" : > "$OUT" ATT="$(mktemp /tmp/codex-attempt.XXXXXX)" trap '"'"'rm -f "$ATT"'"'"' EXIT  read_remaining() {   local r=""   if [ -r /workspace/.timer/remaining_secs ]; then     r="$(cat /workspace/.timer/remaining_secs 2>/dev/null)"   fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r="${TASK_BUDGET_SECS:-}"; fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r=86400; fi   echo "$r" }  MIN_REM=90 MAX_RESUMES=15 BASE=20 CAP=240 JIT=25 N=0 rc=1 while : ; do   REM="$(read_remaining)"   if [ "$REM" -le "$MIN_REM" ] 2>/dev/null; then     echo "=== codex: budget exhausted (remaining=${REM}s); stopping ===" | tee -a "$OUT"     break   fi   : > "$ATT"   if [ "$N" -eq 0 ]; then     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=high -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-…4780 tokens truncated…get 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. 
root      1686  0.0  0.0  12852  5976 ?        Ss   01:22   0:00 bash /workspace/dev_eval.sh /tmp/sel_lenblock.json
root      1766  0.1  0.0  12852  5780 ?        Ss   01:22   0:00 /bin/bash -c sleep 60; cat /tmp/dev.json; ps aux | rg train_nano.py | tail -2
root      1779  100  0.4 28733200 5299844 ?    Sl   01:23   0:27 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
root      1836  0.0  0.0  12852  5500 ?        Ss   01:23   0:00 /bin/bash -c ps aux | rg 'train_nano|dev_eval'; stat -c %y /tmp/dev.json; cat /tmp/dev.json
root      1838  0.0  0.0  17932 11420 ?        S    01:23   0:00 rg train_nano|dev_eval
2026-08-04 01:20:41.208785486 +0000
{"train_npy": "/tmp/dev_train.npy", "heldout_ppl": 469.92328973766223, "mean_loss": 6.152569468059237, "max_iters": 3000, "seed": 1337, "train_s": 55.8}

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{"train_npy": "/tmp/dev_train.npy", "heldout_ppl": 469.97297685307626, "mean_loss": 6.152675196993858, "max_iters": 3000, "seed": 1337, "train_s": 55.6}

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ModuleNotFoundError: No module named 'sklearn'

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aff percentiles [-0.85663144 -0.52918968 -0.40891873 -0.27101443 -0.14001113  0.44782716]
0.15 [(20, 707, -0.188624491443318), (119, 1006, -0.1899056722746047), (85, 2253, -0.21132876853343208), (66, 1219, -0.2227871861303448), (136, 2776, -0.20028133865105985), (21, 1290, -0.24720227383787144), (51, 1351, -0.23982214394904122), (171, 4118, -0.19654222513673075), (42, 2575, -0.24619552002368783), (192, 25115, -0.19157688528669758)] 15828
0.3 [(646, 1006, -0.04978193720378153), (1099, 4245, 0.02750343801383396), (20, 707, -0.188624491443318), (1703, 1060, 0.11477039224092721), (456, 3722, -0.12446598612340148), (119, 1006, -0.1899056722746047), (414, 1591, -0.1387811475605442), (266, 6255, -0.1662228335390528), (136, 2776, -0.20028133865105985), (85, 2253, -0.21132876853343208)] 16782
0.5 [(1703, 1060, 0.11477039224092721), (1099, 4245, 0.02750343801383396), (646, 1006, -0.04978193720378153), (456, 3722, -0.12446598612340148), (414, 1591, -0.1387811475605442), (20, 707, -0.188624491443318), (703, 1135, -0.11455107229891583), (266, 6255, -0.1662228335390528), (119, 1006, -0.1899056722746047), (410, 1827, -0.1597371813015285)] 18076
0.8 [(1703, 1060, 0.11477039224092721), (1099, 4245, 0.02750343801383396), (646, 1006, -0.04978193720378153), (2027, 2952, 0.013624766600138121), (456, 3722, -0.12446598612340148), (1512, 602, -0.05418257640151616), (2404, 1950, 0.006124086497894405), (703, 1135, -0.11455107229891583), (2264, 974, -0.006782548806439353), (414, 1591, -0.1387811475605442)] 21167

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 456 'Discipline won Test, says Pollock\nPORT OF SPAIN, Trinidad, March 21 (AFP) - South Africa beat the West Indies by 69 runs in the second Test at Queens Park Oval\nWednesday in what South African captain Shaun Pollock described as "the most intense Test match I have played in."\nPollock said his team had won because "we stuck to our disciplines and our game plan."\nStarting the last day needing another 200 runs to win with nine wickets standing, the West Indies quickly lost four wickets for 19 runs to crash\nto 51 for five.\nSuch a good start to the day was a bonus, admitted Pollock, but he said his players showed their worth during a 92-run stand between\nRamnaresh Sarwan and West Indian captain Car'

 646 "KANDAHAR, Afghanistan (AP) — Two suicide bombers penetrated a government compound in the country's south Sunday, killing three people, Afghan officials said.\nThe district chief of Spin Boldak said the two militants were targeting a meeting of local officials at a compound in a district of Kandahar province near the Pakistani border. There were no reports of foreign troops or civilians at the site.\nMohammad Hashim said the two attackers arrived in a car, killed a guard and entered the facility firing weapons before blowing themselves up along with their vehicle. The compound houses offices of the district chief and district council as well as other government buildings.\nThe spokesman for the "

 1099 '- 1 Rajasthan Abkari Vibhag Constable Recruitment 2017\n- 1.1 Rajasthan Abkari Vibhag Bharati Recruitment 2017 Notification PDF\n- 1.2 Latest Rajasthan Police Constable Jobs 2017 – Raj Abkari Vibhag Vacancy\n- 1.3 Eligibility Criteria for Raj Abkari Vibhag Excise Constable Jobs 2017\n- 1.4 Rajasthan Abkari Vibhag Bharti 2017 Constable Vacancy Application Fee\n- 1.5 How to Apply for Rajasthan Excise Police Constable Jobs 2017\n- 1.6 Selection Procedure for Raj Excise Vibhag Constable Recruitment 2017\n- 1.7 Download Rajasthan Excise Constable Recruitment 2017 Notification PDF\nRajasthan Abkari Vibhag Constable Recruitment 2017\nRaj Abkari Vibhag Excise Constable Vacancies 2017 Apply Online: As per the'

 1512 "Phoenix Hill Historic District, 922 East Madison Street (House), Louisville, Jefferson County, KY\nSignificance: Working-class dwelling in shotgun style. // 1885. Vernacular. Brick. Shotgun - one story, Approx. 15' x 70'. Exterior features missing - one side wall partly missing. Deteriorated - vacant.\nSurvey number: HABS KY-163-R\nHistoric American Buildings Survey, creator\nLouisville-Jefferson County, 38.25266, -85.75846\nLibrary of Congress\nNo known restrictions on images made by the U.S. Government; images copied from other sources may be restricted. http://www.loc.gov/rr/print/res/114_habs.html"

 1703 'Chennai, April 22 (IANS) In the order issued by the Government of Tamil Nadu, a coordinating committee headed by Chief Minister M.K Stalin has been set up to host the 44th Chess Olympiad, scheduled to be held from July 28 to August 10 in Mahabalipuram.\nAs per the Tamil Nadu government’s order chief minister’s son Udhayanidhi Stalin is also included in the 23 members coordination committee.\nOn Thursday, Stalin informed the Tamil Nadu assembly that a committee will be formed for the successful holding of the Chess Olympiad.\nThe other members of the coordination committee are E.V Velu, Minister for Public Works and Highways, Siva. V. Meyyanathan, Minister for Environment and Sports, M. Mathiven'

 2027 'From Wikipedia, the free encyclopedia\n|February 24, 1882 - December 16, 1948 (aged 66)|\nGeneral Hayao Tada\n|Place of birth||Sendai, Miyagi, Japan|\n|Allegiance||Empire of Japan|\n|Service/branch||Imperial Japanese Army|\n|Years of service||1903 -1945|\n|Commands held||China Garrison Army.|\nIJA 11th Division, IJA Third Army, North China Area Army\nSecond Sino-Japanese War\n|Awards||Order of the Golden Kite (2nd class)|\nTada graduated from the 25th class of the Army War College (Japan) in 1913. He served as an instructor to the Chinese Army at the Beijing Military Academy from 1926-1927. After returning to Japan, he served as an instructor at the Army War College from 1927-1928, before being give a '

 2404 "North Korea spent $42 million on promoting Kim cult: report\n- Navjot Sidhu: Quit RS because I was told to stay away from Punjab\n- Chinkara poaching case: Salman Khan acquitted by Rajasthan High Court\n- SC issues notice to Vijay Mallya on bank plea seeking contempt proceedings\n- Journalists' visa issue: Chinese media warns India of repercussions\n- Parliament LIVE: Speaker Mahajan advises Mann not to attend proceedings till decision arrived at\nNorth Korea spent USD 41.5 million this year promoting the personality cult surrounding the ruling Kim dynasty, a South Korean government report said today.\nA cool USD 9.5 million was dropped on new statues of founder Kim Il-Sung and his late son Kim Jon"

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{"chunk_id":"a19a4d","wall_time_seconds":30.001415588,"session_id":16028,"original_token_count":62}Token indices sequence length is longer than the specified maximum sequence length for this model (1043 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy

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