gpt-5.6-sol

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Token indices sequence length is longer than the specified maximum sequence length for this model (1048 > 1024). Running this sequence through the model will result in indexing errors
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{"chunk_id":"09d52b","wall_time_seconds":1.001772678,"session_id":23860,"original_token_count":0,"output":""}

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{"chunk_id":"054ab4","wall_time_seconds":1.001846543,"session_id":85026,"original_token_count":0,"output":""}

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{"train_npy": "/tmp/canddiv.npy", "heldout_ppl": 308.63715169839674, "mean_loss": 5.732166320558578, "max_iters": 3000, "seed": 1337, "train_s": 55.9}2437

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274c073cca9348c580e8f016f467dc12a5106a4cccb3601f6ecee0a7cdbf5c2b  /tmp/cand.npy
2146c2831b4062a5e14550556e96885d93dac85ce4fa0930d26c5e920b1b76c5  /tmp/cand4.npy
db196ad952113c22c313c508fc8fa0e5d1521e1c89a1c0b2f410548ca7342bb6  /tmp/candw.npy
db196ad952113c22c313c508fc8fa0e5d1521e1c89a1c0b2f410548ca7342bb6  /tmp/canddiv.npy
47145bc79f65b715e41ff15cd7c794e7b7f412368b586c719c124bb4030416f9  /tmp/v4eq.json
4daf7eb6dc09617686a41b620c935176d2fbe2091c200673afa8abd4a71b691b  submission/selection.json
/tmp/v3sel.json 20240 [87261, 66305, 58452, 163054, 103406, 54931, 7625, 37064]
/tmp/v4eq.json 19748 [143847, 66305, 79735, 28721, 1703, 81027, 58452, 88358]
submission/selection.json 19313 [143847, 66305, 79735, 28721, 1703, 81027, 58452, 40000]

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Warning: truncated output (original token count: 6146)
Total output lines: 8

/tmp/candw.npy 2026-08-04 01:54:31.359215315 +0000
/tmp/canddiv.npy 2026-08-04 01:58:27.741662543 +0000
root       225  0.0  0.0  12568  4544 ?        Ss   01:10   0:00 bash -c  rm -f /tmp/ri-exec-459abbf597ec40b6a704e8afeab7f36c.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.…5146 tokens truncated…h, 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      6959  0.0  0.0  12852  5488 ?        Ss   01:59   0:00 /bin/bash -c stat -c '%n %y' /tmp/candw.npy /tmp/canddiv.npy; ps aux | rg 'pack_selection|assemble'
root      6962  0.0  0.0  17932  9972 ?        S    01:59   0:00 rg pack_selection|assemble

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True 0 []

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2026-08-04 01:53:43.232707636 +0000 136769
diff 7 88358 40000

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{}

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{"chunk_id":"9d839c","wall_time_seconds":1.001440617,"session_id":95795,"original_token_count":0,"output":""}

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Token indices sequence length is longer than the specified maximum sequence length for this model (1048 > 1024). Running this sequence through the model will result in indexing errors
0 44276
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6144 44276
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24576 44276

Script running with cell ID 151
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252293f8501f561b568cadee2ec67f26b26c0d2e7daa0e9eaa8ac1cb1815b0f6  /tmp/mild.json
47145bc79f65b715e41ff15cd7c794e7b7f412368b586c719c124bb4030416f9  /tmp/v4eq.json

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{"chunk_id":"1a901c","wall_time_seconds":1.001511699,"session_id":32156,"original_token_count":0,"output":""}

Script running with cell ID 153
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Token indices sequence length is longer than the specified maximum sequence length for this model (1903 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 19625 ids -> /tmp/mild.npy

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{"chunk_id":"21d302","wall_time_seconds":1.002443463,"session_id":15624,"original_token_count":0,"output":""}