gpt-5.6-sol

Script completed
Wall time 0.1 seconds
Output:
{"train_npy": "/tmp/cand1.npy", "heldout_ppl": 488.7383749640834, "mean_loss": 6.191827325776975, "max_iters": 3000, "seed": 1337, "train_s": 51.4}

Script running with cell ID 40
Wall time 11.0 seconds
Output:

Script running with cell ID 41
Wall time 11.0 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
Warning: truncated output (original token count: 6075)
Total output lines: 7

  190       10:40  0.0 bash -c  rm -f /tmp/ri-exec-f21a129984394d2e946fa1a3f3cf2834.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=medium -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 po…5075 tokens truncated…(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. 
 1580       00:34  100 python pack_selection.py /tmp/random.json /tmp/random.npy
 1660       00:00  0.0 /bin/bash -c ps -eo pid,etime,pcpu,cmd | rg 'pack_selection|multi_dev.npy' || true
 1662       00:00  0.0 rg pack_selection|multi_dev.npy

Script completed
Wall time 0.0 seconds
Output:
88144 7.744 5000 ' The Vault\nMuch of the following text comes from the timeline in Fallo'
18404 7.203 5000 'Since I was in about second grade, I have been an obsessive reader and'
95076 7.913 5000 '<|endoftext|>|download that your file may only be then on our residue.'
79372 7.986 3811 'They must hold based this from tools just in the Guests of the downloa'
33023 7.578 5000 'Paleo-Indians migrated from Eurasia to what is now the U.S. mainland a'
0 8.67 65 '|Viewing Single Post From: Spoilers for the Week of February 11th|\n|Li'
3321 7.605 51 "Flash SALE — FREE US Shipping & 50%-70% Off!⚡\nBella's Breast Boosting "
176994 8.659 1142 ' a Free Quote<|endoftext|>Why Buy Used? | VW Dealer Near New Haven ^\nS'
134306 9.86 251 ' XML Sitemap\nXML Sitemap\nThis is a XML Sitemap which is supposed to be'
150287 8.13 4643 ' Analysis and Research Units\nThis is the blog of the Irish Climate Ana'

Script running with cell ID 43
Wall time 11.0 seconds
Output:

Script completed
Wall time 0.0 seconds
Output:
{}

Script running with cell ID 45
Wall time 11.0 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
Warning: truncated output (original token count: 6133)
Total output lines: 9

  190       11:31  0.0 bash -c  rm -f /tmp/ri-exec-f21a129984394d2e946fa1a3f3cf2834.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=medium -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 registe…4933 tokens truncated…e   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. 
 1680       00:39  101 python train_nano.py --train_npy /tmp/random.npy --heldout_npy data/multi_dev.npy --out_json /tmp/random_out.json --max_iters 3000 --seed 1337
 1744       00:14  108 python -u submission/curate.py
 1775       00:00  0.0 /bin/bash -c ps -eo pid,etime,pcpu,cmd | rg 'train_nano|submission/curate.py' || true; test -f /tmp/random_out.json && cat /tmp/random_out.json; cat .timer/remaining_secs
 1777       00:00  0.0 rg train_nano|submission/curate.py
4709

Script completed
Wall time 10.1 seconds
Output:
{"train_npy": "/tmp/random.npy", "heldout_ppl": 476.0103154648885, "mean_loss": 6.165439525141328, "max_iters": 3000, "seed": 1337, "train_s": 50.9}

Script completed
Wall time 0.1 seconds
Output:
  PID     ELAPSED %CPU
 1744       00:36 98.8
-rw-r--r-- 1 root root 317784 23:33:48 submission/selection.json

Script running with cell ID 49
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.8 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
  PID     ELAPSED %CPU
 1744       01:16 95.8
-rw-r--r-- 1 root root 317784 23:33:48 submission/selection.json

Script running with cell ID 51
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.1 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
  PID     ELAPSED %CPU
 1744       01:57 95.4
-rw-r--r-- 1 root root 317784 23:33:48 submission/selection.json

Script running with cell ID 53
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
  PID     ELAPSED %CPU
 1744       02:37 94.8
-rw-r--r-- 1 root root 317784 23:33:48 submission/selection.json

Script completed
Wall time 10.1 seconds
Output:
-rw-r--r-- 1 root root 317320 23:39:48 submission/selection.json

Script running with cell ID 56
Wall time 11.0 seconds
Output:

Script completed
Wall time 0.1 seconds
Output:
 2069       00:16 79.0 python pack_selection.py submission/selection.json /tmp/cand2.npy
 2115       00:01  4.0 /bin/bash -c ps -eo pid,etime,pcpu,cmd | rg 'cand2.npy' || true
 2117       00:01  1.0 rg cand2.npy

Script completed
Wall time 8.4 seconds
Output:
97212 4211  webpage.<|endoftext|>Digital marketing books are all over the internet and in your local bookstores and you should look at all of them and pick the b
92227 17899 tv [untitled] October 6, 2011 4:00am-4:30am PDT the neighborhood outreach that we have conducted has been amazing. there is a website for community me
101918 4158 The Branches of the Government Essay Example The United States government is made up of three different branches. These three branches had responsibil
14955 11935 Meredith Farkas has written a thought provoking post: The essence of Library 2.0? | Information Wants To Be Free which I wanted to comment on. Probabl
6668 2657 When it is buying a new home that you will be doing that it is considered to be big investment. A house that has been inspected thoroughly is what you
106182 26164  Secrets Of Money - When Money Is Corrupted In this video, Mike Maloney talks about his travel to Berlin and Frankfurt where the world's largest Centr
55818 9475 ugby was invented at Rugby school in England during the early 1800s. Since then, it has grown into a game played in 121 countries by 8.5 million peopl
19060 3519 It is a well known fact that online colleges offer a wide variety of different programs for people to choose from. These colleges offer programs that 
49192 2376 A pool building company is the type of company that deals with offering the services of building pools to those who are interested. You should thus co
45266 766 'm interested in approaches that avoids code in the code behind. In my opinion, there are some cases where code must be placed in the code behind. For
11593 27499 Cost benefits of the security and systems management of electronic publishing Internet Web server subscription services and e-commerce. This study dea
47954 4107  on what alimony is If you know someone that has gone through the process of divorcing, you surely know how difficult that is. Maybe, you are the one 
59981 26891 Let?s talk about application. What does this look like for you and for me. The first is this principle of, you might write the word in, accountability
44081 2976 <|endoftext|>Finding the right partner through online dating platform is no more a challenge. With so many online dating apps and tons of dating tips 
45981 20010 <|endoftext|>The simple origin, however, did not prevent Andrei Snesarev from revealing his talents as a scientist, teacher and military leader. After
24701 3657 April 20th was the start of Fuel Church in Falaknuma!! There has not been a church in this village for over 400 years!! There is still about $1,500 th
54906 49862 ests: Steve McMahon, Todd Harris, John Harris, Admiral Thad Allen, Ed Overton CHRIS MATTHEWS, HOST: President Obama takes responsibility. Let?s play H
49605 27267 <|endoftext|>This article considers the interpretation of Jeremiah 7:22. This chronicle draws attention to a number of significant and important studi
8373 26737 This is the last week. How are y?all feeling? Are you feeling good? This is about the time I used to ask this question. How many of you were not here 
71186 2261  I came across a story about a book challenge in a public library in Kentucky. The scary part? It was a library staffer challenging the book. The situ
92848 7368 , March 06, 2007 A page describing what's available on this web site. Revised not very Think of this as a very inadequate and outdated tour guide. It 
112570 2669  has the potential for true genius. No?j is usually known for its grounded ideas, the practicality with which knowledge is applied to problem solving.
36320 21083 Building the Ecology of Libraries ? An Interview with Brewster Kahle This interview is cross-posted here and on the OKFN blog. At OKCon 2011, we had t
84733 4187  Self-Awareness: It is the experience of accepting things as they are. It can be present in the moment, pain-free, or large. It is also a state of bei
Token indices sequence length is longer than the specified maximum sequence length for this model (3793 > 1024). Running this sequence through the model will result in indexing errors