gpt-5.6-sol

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{"chunk_id":"508133","wall_time_seconds":30.001155949,"session_id":71728,"original_token_count":0}

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{}

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root       223  0.0  0.0  12568  6380 ?        Ss   23:26   0:00 bash -c  rm -f /tmp/ri-exec-e3287205fea24aa4848f291d2a1c8c75.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 accumu…5211 tokens truncated…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      1074  0.1  0.0  12852  5988 ?        Ss   23:32   0:00 /bin/bash -c kill 672 2>/dev/null || true; python /workspace/submission/curate.py; ls -lh /workspace/submission/selection.json; cat /workspace/.timer/remaining_secs
root      1075 95.7  0.0 394284 386628 ?       R    23:32   1:06 python /workspace/submission/curate.py
root      1181  0.0  0.0  12852  5004 ?        Ss   23:34   0:00 /bin/bash -c ps aux | rg '/workspace/submission/curate.py' || true; ls -lh /workspace/submission/selection.json 2>/dev/null || true
root      1183  0.0  0.0  17932 10636 ?        S    23:34   0:00 rg /workspace/submission/curate.py

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-rw-r--r-- 1 root root 46K Aug  3 23:37 /workspace/submission/selection.json

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{
  "documents": 6552,
  "tokens_by_domain": {
    "encyclopedic": 3450935,
    "general": 3450803,
    "news": 3450142,
    "tech": 3457089
  }
}
-rw-r--r-- 1 root root 46K Aug  3 23:37 /workspace/submission/selection.json
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