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-rw-r--r-- 1 root root 4950 Aug  4 01:15 /workspace/submission/curate.py

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root       256  0.0  0.0  12568  5452 ?        Ss   01:11   0:00 bash -c  rm -f /tmp/ri-exec-b5427a18159f4dc3bf4768125dd764ec.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 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)…4298 tokens truncated…s 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. 
root       907  0.1  0.0  12852  5904 ?        Ss   01:15   0:00 /bin/bash -c ls -l /workspace/submission/curate.py && time python3 /workspace/submission/curate.py --output /tmp/sel_q1.json python3 - <<'PY' import json s=json.load(open('/tmp/sel_q1.json')) print(s[:50]); print('min/max first15k',min(s[:15000]),max(s[:15000]),sum(i<30000 for i in s[:15000]),sum(i>100000 for i in s[:15000])) # show top samples want=set(s[:12]) for line in open('/workspace/data/pool.jsonl'):  d=json.loads(line)  if d['id'] in want: print(d['id'],len(d['text']),repr(d['text'][:220].replace('\n',' '))) PY
root       909 94.4  0.0  42012 35000 ?        R    01:15   0:32 python3 /workspace/submission/curate.py --output /tmp/sel_q1.json
root       967  0.0  0.0  12852  5892 ?        Ss   01:16   0:00 /bin/bash -c ps aux | rg curate.py; ls -lh /tmp/sel_q1.json 2>/dev/null; cat /workspace/.timer/remaining_secs
root       969  0.0  0.0  17932 11240 ?        S    01:16   0:00 rg curate.py
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-rw-r--r-- 1 root root 1.3M Aug  4 01:18 /tmp/sel_q1.json

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exec_command failed for `/bin/bash -lc 'rm -f /tmp/dev.json; bash /workspace/dev_eval.sh /tmp/sel_q1.json'`: CreateProcess { message: "Rejected(\"`/bin/bash -lc 'rm -f /tmp/dev.json; bash /workspace/dev_eval.sh /tmp/sel_q1.json'` rejected: rm -f style commands are not permitted. Use a safer approach\")" }

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SESSION_ID=25839

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{"chunk_id":"b1c2ae","wall_time_seconds":5.001719164,"session_id":25839,"original_token_count":46}Token indices sequence length is longer than the specified maximum sequence length for this model (1668 > 1024). Running this sequence through the model will result in indexing errors

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{"chunk_id":"05d672","wall_time_seconds":30.001222866,"session_id":25839,"original_token_count":16}packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy

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root      1414  0.2  0.0  12852  5740 ?        Ss   01:19   0:00 /bin/bash -c sleep 30; ps aux | rg 'train_nano.py' | tail -2; cat /tmp/dev.json
root      1456  0.0  0.0  17932  9780 ?        S    01:20   0:00 rg train_nano.py
{"train_npy": "/tmp/dev_train.npy", "heldout_ppl": 466.0340428130419, "mean_loss": 6.144258684704258, "max_iters": 3000, "seed": 1337, "train_s": 55.4}

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{"chunk_id":"9f60be","wall_time_seconds":5.001621054,"session_id":25839,"original_token_count":0}