gpt-5.6-sol

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{"train_npy": "/tmp/cand3.npy", "heldout_ppl": 525.5146896501966, "mean_loss": 6.264378143589862, "max_iters": 3000, "seed": 1337, "train_s": 51.3}

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  190       21:29  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…5072 tokens truncated…PU 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. 
 2889       00:16 92.1 python -u submission/curate.py
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65676 17499 <|endoftext|>- Case report - Open Access Successful treatment of highly recurrent facial baroparesis in a frequent high-altitude t
112044 3560 To all those who journey by autos in Hyderabad.. do you might have a problem free journey at all times? Folks from all over the wo
32838 2028 <|endoftext|>We helped change the way the BBC reports on political issues. Disproportionate level of coverage Professor Justin Lew
43784 2252 To develop an empirical formula I used an Unihedron "Sky Quality Meter - L" to measure the brightness of the night sky in SQM unit
94333 2391 ").<|endoftext|>|? UNICEF video| |Demobilized child soldiers get off a truck after being released in Burundi.| By Olalekan Ajia BU
105279 596 aaa).<|endoftext|>In the multimedia section you can watch videos of interviews with artists who have shown in the Fundaci? Vila Ca
61495 3755 Many resolutions fall by the wayside in a matter of weeks because they are much grander in scale than what can realistically be ac
4181 1706 "According to police records, 31 people were injured, of which 14 were Muslims and 17 Sinhalese. Police have received 138 complain
54730 1698 If you're looking to dispel some persistent American Idol urban legends, This section of the WhatNotToSing.com database is the onl
37019 2174 <|endoftext|>Car Free Day events taking place in Ghajnsielem village |Email item||Print item|| As part of the Annual Car Free Day 
80803 6867 How to Implement a New Strategy Without Disrupting Your Organization Throughout most of modern business history, corporations have
10946 1776 Overview: Remember those 1970s Sunday night murder mysteries: McMillan and Wife, McCloud and Columbo? Well, I believe the McBride 
109460 1783 <|endoftext|>NASA Leaders Hon Raila Odinga and Hon. Kalonzo Musyoka have applauded the move by President Uhuru Kenyatta to reduce 
72441 860  program<|endoftext|>Sunday Links ? 12/23/12 Happy Holidays Cigar Smokers! We here at A Cigar Smoker have a little more to celebra
30254 2340 Baltimore, Md.-- Corrigan Sports Enterprises, Inc., (CSE) the creator of the Under Armour All-America Lacrosse Classic, announced 
21892 1339 Hey friends, I've got some more stellar news. We've got another new project in the works, this one is for our new best friends, Si

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 3156       00:17  0.2 /bin/bash -c while pgrep -f '^python pack_selection.py submission/selection.json /tmp/cand4.npy$' >/dev/null; do sleep 5; done; python train_nano.py --train_npy /tmp/cand4.npy --heldout_npy data/multi_dev.npy --out_json /tmp/cand4.json --max_iters 3000 --seed 1337
 3192       00:00  0.0 /bin/bash -c ps -eo pid,etime,pcpu,cmd | rg 'cand4' || true
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3156 python train_nano.py --train_npy /tmp/cand4.npy --heldout_npy data/multi_dev.npy --out_json /tmp/cand4.json --max_iters 3000 --seed 1337

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{"train_npy": "/tmp/cand4.npy", "heldout_ppl": 446.0069866233424, "mean_loss": 6.100334616972127, "max_iters": 3000, "seed": 1337, "train_s": 50.9}

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{}

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{'ids': 35000, 'unique': 35000, 'min': 2, 'max': 182015, 'packed_tokens': 12000000, 'selection_bytes': 319099}
13862ef99cc6c7417bdf1f01ea8a70a32eb8e1043818f429e8f79b84074c9803  submission/selection.json
3ff7c5a61bade6bb889232c1c4961f7e74d1b47fe6775bc7216477e741b013b3  submission/curate.py
e8648fcb1803389acf7bb0456253373dc9d4a6328bb59e2e57ac3d80b409cb33  claim.md
{"train_npy": "/tmp/cand4.npy", "heldout_ppl": 446.0069866233424, "mean_loss": 6.100334616972127, "max_iters": 3000, "seed": 1337, "train_s": 50.9}3878