gpt-5.6-sol

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{"chunk_id":"a1da90","wall_time_seconds":30.002398597,"session_id":73518,"original_token_count":0,"output":""}

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256 bash -c  rm -f /tmp/ri-exec-9289a6802a1946e1915b5d323661c446.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=low -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-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 c…4221 tokens truncated…ity 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-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. 
653 /bin/bash -c python submission/curate.py && python pack_selection.py submission/selection.json /tmp/curated.npy && python - <<'PY' import json,numpy as np s=json.load(open('submission/selection.json')); a=np.load('/tmp/curated.npy') print(len(s),len(set(s)),a.shape,a.dtype,min(s),max(s)) print(s[:30]) PY
728 /bin/bash -c python - <<'PY' import runpy x=runpy.run_path('submission/curate.py') print(x['__name__'],x.keys()) x['main']() PY
779 /bin/bash -c python -u submission/curate.py; echo EXIT:$?; ls -l submission
780 python -u submission/curate.py
829 python -u submission/curate.py
1166 /bin/bash -c ps -p 73518 -o pid,etime,pcpu,rss,cmd || true; pgrep -af 'submission/curate.py' || true; ls -l submission
total 90
drwxr-xr-x 1 root root    80 Aug  3 23:29 __pycache__
-rw-r--r-- 1 root root  4555 Aug  3 23:29 curate.py
-rw-r--r-- 1 root root 86914 Aug  3 23:34 selection.json

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{"chunk_id":"533ffa","wall_time_seconds":1.002245053,"session_id":49098,"original_token_count":8,"output":"86914 submission/selection.json\n"}

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{"chunk_id":"c1f2cc","wall_time_seconds":30.001541984,"session_id":49098,"original_token_count":46,"output":"Token indices sequence length is longer than the specified maximum sequence length for this model (4792 > 1024). Running this sequence through the model will result in indexing errors\n"}

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{"chunk_id":"8435d3","wall_time_seconds":0.049376867,"exit_code":0,"original_token_count":15,"output":"packed 12000000 tokens from 12134 ids -> /tmp/curated.npy\n"}

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{"chunk_id":"8ab93e","wall_time_seconds":55.445824022,"exit_code":0,"original_token_count":16,"output":"HELDOUT_PPL 473.441  loss 6.1600  train_s 54  /tmp/curated.npy\n"}

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85070 19455  to Ayita at tda for this lovely ci Chapter 19. Beautiful? Me? Desperate times call for desperate measures. And desperate measures right now include uninhibited, self-absorbed sobb
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{"train_npy": "/tmp/curated.npy", "heldout_ppl": 473.44058732141775, "mean_loss": 6.160026429191468, "max_iters": 3000, "seed": 1337, "train_s": 54.0}

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{}

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{"chunk_id":"6f0d67","wall_time_seconds":1.000817823,"session_id":64748,"original_token_count":0,"output":""}

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{"chunk_id":"aa6798","wall_time_seconds":3.30760885,"exit_code":1,"original_token_count":178,"output":"Traceback (most recent call last):\n  File \"/workspace/score_gpt.py\", line 8, in <module>\n    m=AutoModelForCausalLM.from_pretrained('gpt2').cuda().eval()\n  File \"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\", line 604, in from_pretrained\n    return model_class.from_pretrained(\n  File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 277, in _wrapper\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 4924, in from_pretrained\n    if is_from_file and not is_sharded and checkpoint_files[0].endswith(\".safetensors\"):\nAttributeError: 'NoneType' object has no attribute 'endswith'\n"}

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{"chunk_id":"1f1c6a","wall_time_seconds":1.001520744,"session_id":57928,"original_token_count":10,"output":"100000 9135\n120000 10520\n140000 11032\n"}

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