claude-opus-5

{
  "command": "TOKENIZERS_PARALLELISM=false python pack_selection.py submission/selection.json /tmp/fin3.npy 2>&1|grep packed; python train_nano.py --train_npy /tmp/fin3.npy --heldout_npy data/multi_dev.npy --out_json /tmp/fin3.json 2>&1|grep HELDOUT; cat .timer/remaining_secs"
}

{
  "command": "python - <<'EOF'\ns=open('submission/curate.py').read()\ns=s.replace(\"TOP_FRAC = 0.28      # candidate pool = top 28% of the ranking\",\"TOP_FRAC = 0.20      # candidate pool = top 20% of the ranking\")\ns=s.replace(\"All knobs (top fraction 0.28,\",\"All knobs (top fraction 0.20,\")\nopen('submission/curate.py','w').write(s)\nEOF\nnohup bash -c 'python submission/curate.py && TOKENIZERS_PARALLELISM=false python pack_selection.py submission/selection.json /tmp/fin4.npy && python train_nano.py --train_npy /tmp/fin4.npy --heldout_npy data/multi_dev.npy --out_json /tmp/fin4.json' > /tmp/fin4.log 2>&1 &\nsleep 115; tail -2 /tmp/fin4.log"
}

{
  "command": "sleep 115; tail -3 /tmp/fin4.log; cat .timer/remaining_secs"
}

{
  "command": "sleep 105; grep -E \"packed|HELDOUT\" /tmp/fin4.log; cat .timer/remaining_secs"
}

{
  "command": "python - <<'EOF'\ns=open('claim.md').read()\ns=s.replace(\"\"\"| whole-doc mean score + temperature sampling (T=1, top 20%) | 367.3 |\n| ... same but narrower/sharper (top 12%, T=0.6) | 376.0 |\n| ... same but broader (top 35%, T=1.0) | 370.4 |\n| **whole-doc 0.5·mean + 0.5·worst window + T=1 sampling from top 20%** | **362.0** |\"\"\",\n\"\"\"| whole-doc mean score + temperature sampling (T=1, top 20%) | 367.3 |\n| ... same but narrower/sharper (top 12%, T=0.6) | 376.0 |\n| ... same but broader (top 35%, T=1.0) | 370.4 |\n| **whole-doc 0.5·mean + 0.5·worst window + T=1 sampling (shipped rule)** | **362.0 / 362.2 / 368.4** (top 20%, three draws) |\n| same rule, top 28% slice | 359.1 / 367.9 / 370.5 (three draws) |\n| same rule, worst-window weight 0.8 (top 20%) | 373.4 |\n\nThe last three rows are the measured **draw-to-draw spread of the shipped rule**:\nre-running the identical criterion (the classifier fit is GPU-nondeterministic and\nthe selection is a random draw) moves dev perplexity by about ±5. That spread is\nlarger than the gap between the 20% and 28% slice settings, so those two are not\ndistinguishable with the evaluations available here; everything above 375 in the\ntable *is* distinguishable from the shipped rule. The shipped `selection.json` is\nthe verbatim output of `curate.py` and measures **368.4** dev ppl, against a\nrandom-selection baseline of **469.9** (−22%).\"\"\")\ns=s.replace(\"\"\"* The worst-window penalty is noise — falsified by re-running with a different\n  sampling seed and observing the 367.3 / 362.0 gap vanish (the gap is ~5 ppl;\n  seed-to-seed noise on this frozen recipe is roughly ±3, so this is the\n  weakest of the three claims and the one most worth re-testing).\"\"\",\n\"\"\"* The worst-window penalty is noise. This is the weakest of the three claims:\n  the mean-vs-mean+worst gap (367.3 → 362.0) is the same size as the ±5\n  draw-to-draw spread measured afterwards, so a single pair of runs does not\n  settle it. Proper test: 5+ draws per aggregation setting, compare\n  distributions. Pushing the weight to 0.8 clearly *hurts* (373.4), which does\n  establish that the aggregation weight matters — just not that 0.5 beats 0.0.\"\"\")\nopen('claim.md','w').write(s)\nEOF\npython -c \"\nimport json;s=json.load(open('/workspace/submission/selection.json'));ids={json.loads(l)['id'] for l in open('/workspace/data/pool.jsonl')}\nprint('ids',len(s),'unique',len(set(s))==len(s),'in-range',set(s)<=ids)\"; ls -la submission/ claim.md"
}

{
  "command": "python - <<'EOF'\ns=open('claim.md').read()\ns=s.replace(\"469.9 vs 362.0, a 23% reduction\",\"469.9 vs 368.4 for the shipped file, 362.0–368.4 across draws — a 22% reduction\")\ns=s.replace(\"\"\"* **Segment-level purity matters beyond average quality.** Adding the\n  worst-window term to an already whole-document score moves 367.3 → 362.0. If\n  quality were a document-level constant, the worst-window term would be\n  redundant and change nothing.\"\"\",\n\"\"\"* **Segment-level purity matters beyond average quality.** Scoring the whole\n  document instead of its first 3000 chars is a clear win (389.5 → 375.4 at\n  strict top-K). Weighting the worst window on top of that is directionally\n  positive but inside the draw noise (367.3 → 362.0, spread ±5), while\n  over-weighting it clearly hurts (0.8 → 373.4). If quality were a\n  document-level constant, none of these would move.\"\"\")\ns=s.replace(\"\"\"* **Diversity beats greed at fixed mean quality.** Sampling from the top 20%\"\"\",\n\"\"\"* **Diversity beats greed.** Sampling from the top 20%\"\"\")\nopen('claim.md','w').write(s)\nEOF\nhead -2 submission/curate.py; grep -n \"TOP_FRAC\\|MIN_WEIGHT\\|TEMP =\\|N_SELECT\" submission/curate.py | head -6; cat .timer/remaining_secs"
}