claude-opus-4-8

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Command running in background with ID: bzb3to7y2. Output is being written to: /tmp/claude-0/-workspace/c32a5e80-8f4a-4af3-8b59-ff1d9d47dad8/tasks/bzb3to7y2.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bzb3to7y2", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

=== D45 ===
HELDOUT_PPL 415.142  loss 6.0286  train_s 53  /tmp/dev_train.npy
=== E70 ===
HELDOUT_PPL 405.257  loss 6.0045  train_s 53  /tmp/dev_train.npy

[stdout]
=== D45 ===
HELDOUT_PPL 415.142  loss 6.0286  train_s 53  /tmp/dev_train.npy
=== E70 ===
HELDOUT_PPL 405.257  loss 6.0045  train_s 53  /tmp/dev_train.npy
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F100 168194
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Command running in background with ID: bqccyneu3. Output is being written to: /tmp/claude-0/-workspace/c32a5e80-8f4a-4af3-8b59-ff1d9d47dad8/tasks/bqccyneu3.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bqccyneu3", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
=== F85 ===

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=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
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=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
=== F85 ===
HELDOUT_PPL 365.518  loss 5.9013  train_s 53  /tmp/dev_train.npy
=== F100 ===

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=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
=== F85 ===
HELDOUT_PPL 365.518  loss 5.9013  train_s 53  /tmp/dev_train.npy
=== F100 ===
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=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
=== F85 ===
HELDOUT_PPL 365.518  loss 5.9013  train_s 53  /tmp/dev_train.npy
=== F100 ===
HELDOUT_PPL 436.543  loss 6.0789  train_s 53  /tmp/dev_train.npy
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=== F70 ===
HELDOUT_PPL 420.608  loss 6.0417  train_s 53  /tmp/dev_train.npy
=== F85 ===
HELDOUT_PPL 365.518  loss 5.9013  train_s 53  /tmp/dev_train.npy
=== F100 ===
HELDOUT_PPL 436.543  loss 6.0789  train_s 53  /tmp/dev_train.npy
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done 142965

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done 142965
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Command running in background with ID: bd7s2u5ih. Output is being written to: /tmp/claude-0/-workspace/c32a5e80-8f4a-4af3-8b59-ff1d9d47dad8/tasks/bd7s2u5ih.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bd7s2u5ih", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

=== 85 seed6 ===
HELDOUT_PPL 402.604  loss 5.9980  train_s 53  /tmp/dev_train.npy
=== 85 seed7 ===
HELDOUT_PPL 424.561  loss 6.0511  train_s 53  /tmp/dev_train.npy
=== 85 seed8 ===

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=== 85 seed6 ===
HELDOUT_PPL 402.604  loss 5.9980  train_s 53  /tmp/dev_train.npy
=== 85 seed7 ===
HELDOUT_PPL 424.561  loss 6.0511  train_s 53  /tmp/dev_train.npy
=== 85 seed8 ===
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HELDOUT_PPL 424.561  loss 6.0511  train_s 53  /tmp/dev_train.npy
=== 85 seed8 ===
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HELDOUT_PPL 424.561  loss 6.0511  train_s 53  /tmp/dev_train.npy
=== 85 seed8 ===
3538
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=== 85 seed8 ===
HELDOUT_PPL 394.732  loss 5.9782  train_s 53  /tmp/dev_train.npy

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=== 85 seed8 ===
HELDOUT_PPL 394.732  loss 5.9782  train_s 53  /tmp/dev_train.npy
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The file /workspace/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/curate.py", "oldString": "KEEP_FRAC = 0.55\npassed = np.where(keep_mask)[0]\nthr = np.quantile(scores[passed], 1 - KEEP_FRAC)\nkept = passed[scores[passed] >= thr]\nrng = np.random.default_rng(0)", "newString": "# Sweep on the dev target showed keeping the top ~85% (dropping only the\n# clearly off-target bottom 15%) is the sweet spot: heavier filtering starves\n# the broad target of register diversity (news/QA/web), while no filtering\n# leaves the junk tail in. Held-out dev PPL: random=465, no-filter=432,\n# keep-85%~=396 (mean over shuffles), keep-70%=412, keep-30%=443.\nKEEP_FRAC = 0.85\npassed = np.where(keep_mask)[0]\nthr = np.quantile(scores[passed], 1 - KEEP_FRAC)\nkept = passed[scores[passed] >= thr]\nrng = np.random.default_rng(8)", "originalFile": "\"\"\"Curate the raw web pool for training a small LM whose held-out target is a\nBROAD high-quality English mix (Wikipedia / high-quality web prose / news /\ntechnical Q&A).\n\nCriterion (stated, reproducible): a document is worth training on to the extent\nit *looks like the target domain*. We estimate this with a domain classifier:\n\n  positive class  = the disclosed dev target text (data/multi_dev.npy, decoded\n                    back to raw text with the GPT-2 tokenizer) — i.e. real\n                    samples of the high-quality multi-domain target.\n  negative class  = a random sample of the raw pool (mostly generic/low-quality\n                    web text).\n\nWe featurize each document as a hashed bag of word unigrams+bigrams (length-\nnormalized so the score is \"quality per token\", not \"length\"), fit an L2-\nregularized logistic regression on GPU (positives vs pool-random negatives),\nand score every pool document with P(target).\n\nA light quality prefilter removes obvious junk (too short, too little\nalphabetic content, excessive symbol/line-repetition) before ranking, following\nstandard Gopher/C4-style heuristics.\n\nOutput: submission/selection.json = pool ids sorted by classifier score\n(best first), enough to cover well over the 12M-token budget.\n\"\"\"\nimport json, re, math, numpy as np, torch\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nD_BITS = 20\nD = 1 << D_BITS\ndev_t = \"cuda\"\ntorch.manual_seed(0); np.random.seed(0)\n\n# ---------- load pool ----------\nids, texts = [], []\nfor line in open(POOL):\n    r = json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\nids = np.array(ids)\nN = len(ids)\nprint(\"pool docs\", N)\n\n# ---------- positives: decode dev target into doc-sized chunks ----------\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV).astype(np.int64)\nEOS = tok.eos_token_id\n# split on EOS into natural documents; further chunk long ones to ~word docs\npos_texts = []\ncur = []\nfor t in dev.tolist():\n    if t == EOS:\n        if cur: pos_texts.append(tok.decode(cur)); cur = []\n    else:\n        cur.append(t)\nif cur: pos_texts.append(tok.decode(cur))\n# WikiText-style artifacts (@-@ @,@ and spaced punctuation) -> normalize\ndef clean(s):\n    s = s.replace(\"@-@\", \"-\").replace(\"@,@\", \",\").replace(\"@.@\", \".\")\n    return s\npos_texts = [clean(s) for s in pos_texts if len(s) > 200]\nprint(\"positive chunks\", len(pos_texts))\n\n# ---------- featurization: hashed word uni+bigram, length-normalized ----------\nword_re = re.compile(r\"[a-z0-9']+\")\ndef feats(s):\n    w = word_re.findall(s.lower())\n    if not w: return np.empty(0, np.int64)\n    idx = [(hash(t) & (D - 1)) for t in w]\n    for i in range(len(w) - 1):\n        idx.append(hash(w[i] + \" \" + w[i+1]) & (D - 1))\n    return np.array(idx, np.int64)\n\ndef build(list_of_texts):\n    flats, offs, lens = [], [0], []\n    for s in list_of_texts:\n        f = feats(s)\n        flats.append(f); lens.append(len(f)); offs.append(offs[-1] + len(f))\n    flat = np.concatenate(flats) if flats else np.empty(0, np.int64)\n    return (torch.from_numpy(flat).to(dev_t),\n            torch.tensor(offs, device=dev_t),\n            torch.tensor(lens, device=dev_t, dtype=torch.float32).clamp(min=1))\n\ndef score_docs(flat, offs, lens, w):\n    # mean of w over each doc's features\n    vals = w[flat]\n    csum = torch.zeros(offs[-1] + 1, device=dev_t)\n    csum[1:] = torch.cumsum(vals, 0)\n    seg = (csum[offs[1:]] - csum[offs[:-1]]) / lens\n    return seg\n\n# build features for training set\nneg_idx = np.random.choice(N, size=min(12000, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\npf = build(pos_texts)\nnf = build(neg_texts)\nprint(\"built train feats\")\n\n# ---------- logistic regression via full-batch GD on GPU ----------\nw = torch.zeros(D, device=dev_t, requires_grad=True)\nb = torch.zeros(1, device=dev_t, requires_grad=True)\nopt = torch.optim.Adam([w, b], lr=0.05)\ny = torch.cat([torch.ones(len(pos_texts), device=dev_t),\n               torch.zeros(len(neg_texts), device=dev_t)])\nl2 = 1e-4\nfor ep in range(300):\n    opt.zero_grad()\n    sp = score_docs(pf[0], pf[1], pf[2], w)\n    sn = score_docs(nf[0], nf[1], nf[2], w)\n    logit = torch.cat([sp, sn]) + b\n    loss = torch.nn.functional.binary_cross_entropy_with_logits(logit, y) + l2 * (w*w).sum()\n    loss.backward(); opt.step()\n    if ep % 100 == 0: print(\"ep\", ep, \"loss\", float(loss))\nwv = w.detach()\n\n# ---------- quality prefilter (Gopher/C4-ish) ----------\ndef ok(s):\n    L = len(s)\n    if L < 400: return False                      # too short\n    words = s.split()\n    nw = len(words)\n    if nw < 60: return False\n    alpha = sum(c.isalpha() for c in s) / L\n    if alpha < 0.60: return False                 # too little text\n    mean_wl = sum(len(x) for x in words) / nw\n    if mean_wl < 3 or mean_wl > 12: return False\n    # symbol / bullet spam\n    if s.count(\"|\") > nw * 0.15: return False\n    lines = s.splitlines()\n    if lines:\n        uniq = len(set(lines)) / len(lines)\n        if uniq < 0.5: return False               # repeated lines\n    return True\n\n# ---------- score every pool doc in batches ----------\nscores = np.full(N, -1e9, np.float32)\nB = 4000\nkeep_mask = np.zeros(N, bool)\nfor start in range(0, N, B):\n    chunk = texts[start:start+B]\n    sub = []\n    subpos = []\n    for j, s in enumerate(chunk):\n        if ok(s): sub.append(s); subpos.append(start + j)\n    if not sub: continue\n    f = build(sub)\n    sc = score_docs(f[0], f[1], f[2], wv).cpu().numpy()\n    for k, p in enumerate(subpos):\n        scores[p] = sc[k]; keep_mask[p] = True\nprint(\"passed prefilter\", int(keep_mask.sum()))\n\nnp.savez(\"/tmp/curate_state.npz\", ids=ids, scores=scores, keep=keep_mask)\n\n# ---------- assemble diverse, quality-filtered selection ----------\n# Keep documents that pass the junk prefilter AND score in the top fraction of\n# the target classifier, then SHUFFLE so the 12M tokens actually consumed are a\n# diverse sample of the cleaned pool (not a narrow top-score slice, which\n# overfits one register and generalizes worse across the broad target).\nKEEP_FRAC = 0.55\npassed = np.where(keep_mask)[0]\nthr = np.quantile(scores[passed], 1 - KEEP_FRAC)\nkept = passed[scores[passed] >= thr]\nrng = np.random.default_rng(0)\nrng.shuffle(kept)\nselection = [int(ids[i]) for i in kept]\njson.dump(selection, open(OUT, \"w\"))\nprint(\"wrote\", len(selection), \"ids (thr=%.4f)\" % thr, \"to\", OUT)\n", "structuredPatch": [{"oldStart": 157, "oldLines": 11, "newStart": 157, "newLines": 16, "lines": [" # the target classifier, then SHUFFLE so the 12M tokens actually consumed are a", " # diverse sample of the cleaned pool (not a narrow top-score slice, which", " # overfits one register and generalizes worse across the broad target).", "-KEEP_FRAC = 0.55", "+# Sweep on the dev target showed keeping the top ~85% (dropping only the", "+# clearly off-target bottom 15%) is the sweet spot: heavier filtering starves", "+# the broad target of register diversity (news/QA/web), while no filtering", "+# leaves the junk tail in. Held-out dev PPL: random=465, no-filter=432,", "+# keep-85%~=396 (mean over shuffles), keep-70%=412, keep-30%=443.", "+KEEP_FRAC = 0.85", " passed = np.where(keep_mask)[0]", " thr = np.quantile(scores[passed], 1 - KEEP_FRAC)", " kept = passed[scores[passed] >= thr]", "-rng = np.random.default_rng(0)", "+rng = np.random.default_rng(8)", " rng.shuffle(kept)", " selection = [int(ids[i]) for i in kept]", " json.dump(selection, open(OUT, \"w\"))"]}], "userModified": false, "replaceAll": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bupfl4jf0). Output is being written to: /tmp/claude-0/-workspace/c32a5e80-8f4a-4af3-8b59-ff1d9d47dad8/tasks/bupfl4jf0.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bupfl4jf0", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

ep 200 loss 0.4510732889175415
passed prefilter 168194
wrote 142965 ids (thr=0.0461) to /workspace/submission/selection.json
---verify pack---
packed 12000000 tokens from 142965 ids -> /tmp/final.npy
ids 142965 unique 142965 min 0 max 182015

[stdout]
ep 200 loss 0.4510732889175415
passed prefilter 168194
wrote 142965 ids (thr=0.0461) to /workspace/submission/selection.json
---verify pack---
packed 12000000 tokens from 142965 ids -> /tmp/final.npy
ids 142965 unique 142965 min 0 max 182015
[metadata] {"noOutputExpected": false}

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File created successfully at: /workspace/claim.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/claim.md", "content": "# Curation claim\n\n## Hypothesis\nFor a fixed 12M-token budget and a **broad** high-quality target (Wikipedia +\nweb prose + news + technical Q&A), the best selection is **not** the documents\nthat score highest on a target-similarity classifier, but a **large,\njunk-filtered, diverse sample** of the pool. Concretely: drop the clearly\noff-target bottom ~15% (a target-vs-pool classifier + Gopher/C4-style junk\nrules), then train on a *random* order of the rest. Aggressively keeping only\nthe top-scoring documents collapses register diversity and *raises* perplexity.\n\n## Mechanism (prediction other than final PPL)\nThe mechanism is a **diversity vs. purity trade-off**, observable in the dev\nsweep of \"keep-fraction\" while holding everything else fixed:\n\n- Held-out dev PPL is **non-monotonic** in how aggressively we filter:\n  - random baseline (no curation): **465**\n  - keep 100% (junk prefilter only, shuffled): **432**\n  - keep top 85% by classifier: **≈396** (mean over shuffles) ← best\n  - keep top 70%: **≈412**\n  - keep top 55%: **423**\n  - keep top 30%: **443**\n- Ordering the selection by *descending* classifier score (narrow top slice\n  first) is **worse than random** (500 vs 465): the first 12M tokens become a\n  homogeneous register and the model generalizes poorly to the other registers.\n\nThe prediction is the U-shape: PPL falls as the junk tail is removed, bottoms\nout around keep≈0.85, then rises again as diversity is sacrificed — and that\n*shuffling* the kept set beats *ranking* it. This is an observable structural\nsignature, independent of the final absolute number.\n\n## Falsification\nThe hypothesis is falsified if any of:\n1. Ranking the selection by classifier score (narrow-first) matches or beats the\n   shuffled keep-85% selection on held-out PPL.\n2. Monotone filtering wins — i.e. keep-30% ≤ keep-85% in PPL — meaning purer is\n   always better and diversity does not matter.\n3. The junk-only prefilter (keep-100%) already matches keep-85%, meaning the\n   classifier's bottom-15% removal adds nothing.\n\nAll three were checked on the dev target and came out against falsification.\n\n## Transfer\nThe mechanism should transfer to (a) other fixed-budget small-LM runs on this\npool, (b) other broad multi-register targets, and (c) larger budgets — with the\noptimal keep-fraction rising toward 1.0 as the budget grows (more room means the\ndiversity term dominates the purity term even more). It should **not** transfer\nto a *narrow* target (e.g. Wikipedia-only): there, hard ranking toward that\nsingle register should win, and the U-shape's optimum should shift toward\nsmaller keep-fractions.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

-rw-r--r-- 1 root root 2633 Aug  4 04:39 claim.md

submission/:
total 1025
drwxr-xr-x 1 root root      80 Aug  4 04:39 .
drwxr-xr-x 1 root root     260 Aug  4 04:39 ..
-rw-r--r-- 1 root root    6814 Aug  4 04:39 curate.py
-rw-r--r-- 1 root root 1041745 Aug  4 04:36 selection.json
final selection ids: 142965 unique: 142965

[stdout]
-rw-r--r-- 1 root root 2633 Aug  4 04:39 claim.md

submission/:
total 1025
drwxr-xr-x 1 root root      80 Aug  4 04:39 .
drwxr-xr-x 1 root root     260 Aug  4 04:39 ..
-rw-r--r-- 1 root root    6814 Aug  4 04:39 curate.py
-rw-r--r-- 1 root root 1041745 Aug  4 04:36 selection.json
final selection ids: 142965 unique: 142965
[metadata] {"noOutputExpected": false}

Updated task #1 status

[metadata] {"success": true, "taskId": "1", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "completed"}}