claude-opus-5

File created successfully at: /workspace/feats2.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/feats2.py", "content": "\"\"\"Line-structure + script features that separate real prose from nav/spam boilerplate.\"\"\"\nimport pickle, re, numpy as np\nfrom multiprocessing import Pool\n\nids, texts = pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))\nN = len(texts)\n_W = re.compile(r\"[A-Za-z']+\")\nKEYS = [\"mean_line_len\", \"frac_lines_ge8w\", \"latin\", \"frac_shortline\",\n        \"wikimark\", \"avg_sent_len\", \"frac_upperline\"]\n\n\ndef feat(rng):\n    out = np.zeros((len(rng), len(KEYS)), dtype=np.float32)\n    for r, i in enumerate(rng):\n        t = texts[i][:30000]\n        lines = [l.strip() for l in t.split(\"\\n\") if l.strip()]\n        if not lines:\n            continue\n        wc = [len(_W.findall(l)) for l in lines]\n        nl = len(lines)\n        letters = sum(ch.isalpha() for ch in t) or 1\n        latin = sum(1 for ch in t if ch.isalpha() and ord(ch) < 384) / letters\n        # wikipedia-ish rendered structure: infobox pipe rows, section furniture\n        wm = (t.count(\"\\n|\") + 3 * t.lower().count(\"external links\")\n              + 3 * t.lower().count(\"references\\n\") + 3 * t.count(\"Category:\")\n              + 2 * t.lower().count(\"see also\"))\n        sents = [s for s in re.split(r\"[.!?]\\s\", t) if len(s) > 5]\n        out[r] = [\n            np.mean([len(l) for l in lines]),\n            np.mean([w >= 8 for w in wc]),\n            latin,\n            np.mean([w <= 3 for w in wc]),\n            min(1.0, wm / 40.0),\n            np.mean([len(_W.findall(s)) for s in sents]) if sents else 0.0,\n            np.mean([l == l.upper() and len(l) > 3 for l in lines]),\n        ]\n    return rng, out\n\n\nA = np.zeros((N, len(KEYS)), dtype=np.float32)\nchunks = [list(range(N))[i::16] for i in range(16)]\nwith Pool(16) as p:\n    for rng, out in p.imap_unordered(feat, chunks):\n        A[rng] = out\nnp.save(\"/tmp/feats2.npy\", A)\nprint(\"done\", A.shape, dict(zip(KEYS, A.mean(0).round(3))))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

superset 92404
Token indices sequence length is longer than the specified maximum sequence length for this model (1244 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1171 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1174 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1142 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1121 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1414 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1199 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1308 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1034 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1287 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3395 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2037 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1484 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1466 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1107 > 1024). Running this sequence through the model will result in indexing errors
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
tokenized 92404
total tokens 97.2M
=== launching feats2
ok

[stdout]
superset 92404
Token indices sequence length is longer than the specified maximum sequence length for this model (1244 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1171 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1174 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1142 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1121 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1414 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1199 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1308 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1034 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1287 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3395 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2037 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1484 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1466 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1107 > 1024). Running this sequence through the model will result in indexing errors
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
tokenized 92404
total tokens 97.2M
=== launching feats2
ok
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/build.py", "content": "\"\"\"Fast variant builder: selection -> packed 12M-token npy straight from the token cache.\n\nUsage: python3 build.py <name> <w_wiki,w_web,w_news,w_qa> <gate:v1|v2> [temp]\n\"\"\"\nimport json, pickle, sys, numpy as np\n\nname = sys.argv[1]\nwts = np.array([float(x) for x in sys.argv[2].split(\",\")])\ngate = sys.argv[3] if len(sys.argv) > 3 else \"v2\"\ntemp = float(sys.argv[4]) if len(sys.argv) > 4 else 0.0\nwts = wts / wts.sum()\n\nBUDGET = 12_000_000\nEOS = 50256\nS = np.load(\"/tmp/scores.npy\")\nok1 = np.load(\"/tmp/ok.npy\")\nst = np.load(\"/tmp/stats.npy\")      # n,stop,dig,nonascii,duplines,uniq,alpha\nf2 = np.load(\"/tmp/feats2.npy\")     # mean_line_len,frac_lines_ge8w,latin,frac_shortline,wikimark,avg_sent_len,frac_upperline\ntc = np.load(\"/tmp/tokcount.npy\")\ncache = pickle.load(open(\"/tmp/tokcache.pkl\", \"rb\"))\nids = np.array(pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))[0])\nN = len(ids)\n\nif gate == \"v1\":\n    ok = ok1.copy()\nelse:\n    # v2: v1 plus line-structure gates that reject nav menus, link farms and\n    # non-Latin spam -- the failure mode seen at the head of the v1 ranking.\n    ok = (ok1\n          & (f2[:, 0] >= 55)        # mean line length: real paragraphs, not menu items\n          & (f2[:, 1] >= 0.40)      # >=40% of lines are >=8 words\n          & (f2[:, 2] >= 0.92)      # Latin script\n          & (f2[:, 3] <= 0.35)      # few <=3-word lines\n          & (f2[:, 5] >= 9.0)       # sentences of plausible length\n          & (f2[:, 6] <= 0.10))     # not ALL-CAPS heavy\n\nok &= tc > 0    # must be in the token cache\nprint(f\"gate {gate}: {ok.sum()} docs, {tc[ok].sum()/1e6:.1f}M tokens\", flush=True)\n\nbest = S.argmax(1)\nshare = BUDGET * wts\nrng = np.random.default_rng(0)\nranked = {}\nfor k in range(4):\n    c = np.where(ok & (best == k))[0]\n    key = S[c, k]\n    if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)\n        key = key / temp + rng.gumbel(size=len(c))\n    ranked[k] = c[np.argsort(-key)]\n    print(f\"  reg{k}: {len(c)} docs {tc[c].sum()/1e6:.1f}M tok\", flush=True)\n\nptr = {k: 0 for k in range(4)}\nper = {k: 0 for k in range(4)}\norder = []\nwhile True:\n    moved = False\n    for k in range(4):\n        if per[k] >= share[k] or ptr[k] >= len(ranked[k]):\n            continue\n        i = int(ranked[k][ptr[k]]); ptr[k] += 1\n        order.append(i); per[k] += int(tc[i]) + 1; moved = True\n    if not moved:\n        break\nprint(\"per-register tokens:\", {k: round(v/1e6, 2) for k, v in per.items()},\n      \" head total %.2fM\" % (sum(per.values())/1e6), flush=True)\n\nhead = set(order)\nsmax = (S * wts).max(1)\ntail = [int(i) for i in np.argsort(-smax) if ok[i] and int(i) not in head]\norder += tail\n\nsel = [int(ids[i]) for i in order]\nassert len(sel) == len(set(sel)), \"dup ids\"\njson.dump(sel, open(f\"/workspace/cand/{name}.json\", \"w\"))\n\nparts, tot = [], 0\nfor i in order:\n    a = cache[i]\n    parts.append(a); tot += len(a) + 1\n    if tot >= BUDGET:\n        break\nflat = np.concatenate([np.concatenate([a, np.array([EOS], dtype=np.uint16)])\n                       for a in parts])[:BUDGET]\nassert len(flat) == BUDGET, len(flat)\nnp.save(f\"/tmp/tr_{name}.npy\", flat)\nprint(f\"{name}: {len(sel)} ids, packed {len(flat)} tokens\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}

done (182016, 7) {'mean_line_len': 182.132, 'frac_lines_ge8w': 0.552, 'latin': 0.998, 'frac_shortline': 0.296, 'wikimark': 0.007, 'avg_sent_len': 36.266, 'frac_upperline': 0.026}
4309

[stdout]
done (182016, 7) {'mean_line_len': 182.132, 'frac_lines_ge8w': 0.552, 'latin': 0.998, 'frac_shortline': 0.296, 'wikimark': 0.007, 'avg_sent_len': 36.266, 'frac_upperline': 0.026}
4309
[metadata] {"noOutputExpected": false}

Updated task #2 description, status

[metadata] {"success": true, "taskId": "2", "updatedFields": ["description", "status"], "statusChange": {"from": "in_progress", "to": "completed"}}

Updated task #3 status

[metadata] {"success": true, "taskId": "3", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "in_progress"}}

reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.02, 1: 3.0, 2: 3.0, 3: 0.88}  head total 9.91M
v2eq: 68093 ids, packed 12000000 tokens

  reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.1, 1: 2.4, 2: 2.4, 3: 0.88}  head total 8.78M
v2wiki: 68093 ids, packed 12000000 tokens

  reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.1, 1: 1.8, 2: 1.8, 3: 0.88}  head total 7.58M
v2wikix: 68093 ids, packed 12000000 tokens

[stdout]
  reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.02, 1: 3.0, 2: 3.0, 3: 0.88}  head total 9.91M
v2eq: 68093 ids, packed 12000000 tokens

  reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.1, 1: 2.4, 2: 2.4, 3: 0.88}  head total 8.78M
v2wiki: 68093 ids, packed 12000000 tokens

  reg2: 7576 docs 5.1M tok
  reg3: 1398 docs 0.9M tok
per-register tokens: {0: 3.1, 1: 1.8, 2: 1.8, 3: 0.88}  head total 7.58M
v2wikix: 68093 ids, packed 12000000 tokens
[metadata] {"noOutputExpected": false}

The file /workspace/build.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/build.py", "oldString": "best = S.argmax(1)\nshare = BUDGET * wts\nrng = np.random.default_rng(0)\nranked = {}\nfor k in range(4):\n    c = np.where(ok & (best == k))[0]\n    key = S[c, k]\n    if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)\n        key = key / temp + rng.gumbel(size=len(c))\n    ranked[k] = c[np.argsort(-key)]\n    print(f\"  reg{k}: {len(c)} docs {tc[c].sum()/1e6:.1f}M tok\", flush=True)\n\nptr = {k: 0 for k in range(4)}\nper = {k: 0 for k in range(4)}\norder = []\nwhile True:\n    moved = False\n    for k in range(4):\n        if per[k] >= share[k] or ptr[k] >= len(ranked[k]):\n            continue\n        i = int(ranked[k][ptr[k]]); ptr[k] += 1\n        order.append(i); per[k] += int(tc[i]) + 1; moved = True\n    if not moved:\n        break", "newString": "HARD = len(sys.argv) > 5 and sys.argv[5] == \"hard\"\nbest = S.argmax(1)\nshare = BUDGET * wts\nrng = np.random.default_rng(0)\nranked = {}\nfor k in range(4):\n    # soft assignment: every register ranks the whole gated pool by ITS OWN score,\n    # so a document that serves two registers is available to both. Hard argmax\n    # assignment starves the scarce registers (wiki/qa) because the generic-web\n    # direction wins the argmax for most of the pool.\n    c = np.where(ok & (best == k))[0] if HARD else np.where(ok)[0]\n    key = S[c, k]\n    if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)\n        key = key / temp + rng.gumbel(size=len(c))\n    ranked[k] = c[np.argsort(-key)]\n    print(f\"  reg{k}: {len(c)} docs {tc[c].sum()/1e6:.1f}M tok\", flush=True)\n\nptr = {k: 0 for k in range(4)}\nper = {k: 0 for k in range(4)}\norder, taken = [], set()\nwhile True:\n    moved = False\n    for k in range(4):\n        if per[k] >= share[k]:\n            continue\n        r = ranked[k]\n        while ptr[k] < len(r):\n            i = int(r[ptr[k]]); ptr[k] += 1\n            if i in taken:\n                continue\n            taken.add(i); order.append(i); per[k] += int(tc[i]) + 1; moved = True\n            break\n    if not moved:\n        break", "originalFile": "\"\"\"Fast variant builder: selection -> packed 12M-token npy straight from the token cache.\n\nUsage: python3 build.py <name> <w_wiki,w_web,w_news,w_qa> <gate:v1|v2> [temp]\n\"\"\"\nimport json, pickle, sys, numpy as np\n\nname = sys.argv[1]\nwts = np.array([float(x) for x in sys.argv[2].split(\",\")])\ngate = sys.argv[3] if len(sys.argv) > 3 else \"v2\"\ntemp = float(sys.argv[4]) if len(sys.argv) > 4 else 0.0\nwts = wts / wts.sum()\n\nBUDGET = 12_000_000\nEOS = 50256\nS = np.load(\"/tmp/scores.npy\")\nok1 = np.load(\"/tmp/ok.npy\")\nst = np.load(\"/tmp/stats.npy\")      # n,stop,dig,nonascii,duplines,uniq,alpha\nf2 = np.load(\"/tmp/feats2.npy\")     # mean_line_len,frac_lines_ge8w,latin,frac_shortline,wikimark,avg_sent_len,frac_upperline\ntc = np.load(\"/tmp/tokcount.npy\")\ncache = pickle.load(open(\"/tmp/tokcache.pkl\", \"rb\"))\nids = np.array(pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))[0])\nN = len(ids)\n\nif gate == \"v1\":\n    ok = ok1.copy()\nelse:\n    # v2: v1 plus line-structure gates that reject nav menus, link farms and\n    # non-Latin spam -- the failure mode seen at the head of the v1 ranking.\n    ok = (ok1\n          & (f2[:, 0] >= 55)        # mean line length: real paragraphs, not menu items\n          & (f2[:, 1] >= 0.40)      # >=40% of lines are >=8 words\n          & (f2[:, 2] >= 0.92)      # Latin script\n          & (f2[:, 3] <= 0.35)      # few <=3-word lines\n          & (f2[:, 5] >= 9.0)       # sentences of plausible length\n          & (f2[:, 6] <= 0.10))     # not ALL-CAPS heavy\n\nok &= tc > 0    # must be in the token cache\nprint(f\"gate {gate}: {ok.sum()} docs, {tc[ok].sum()/1e6:.1f}M tokens\", flush=True)\n\nbest = S.argmax(1)\nshare = BUDGET * wts\nrng = np.random.default_rng(0)\nranked = {}\nfor k in range(4):\n    c = np.where(ok & (best == k))[0]\n    key = S[c, k]\n    if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)\n        key = key / temp + rng.gumbel(size=len(c))\n    ranked[k] = c[np.argsort(-key)]\n    print(f\"  reg{k}: {len(c)} docs {tc[c].sum()/1e6:.1f}M tok\", flush=True)\n\nptr = {k: 0 for k in range(4)}\nper = {k: 0 for k in range(4)}\norder = []\nwhile True:\n    moved = False\n    for k in range(4):\n        if per[k] >= share[k] or ptr[k] >= len(ranked[k]):\n            continue\n        i = int(ranked[k][ptr[k]]); ptr[k] += 1\n        order.append(i); per[k] += int(tc[i]) + 1; moved = True\n    if not moved:\n        break\nprint(\"per-register tokens:\", {k: round(v/1e6, 2) for k, v in per.items()},\n      \" head total %.2fM\" % (sum(per.values())/1e6), flush=True)\n\nhead = set(order)\nsmax = (S * wts).max(1)\ntail = [int(i) for i in np.argsort(-smax) if ok[i] and int(i) not in head]\norder += tail\n\nsel = [int(ids[i]) for i in order]\nassert len(sel) == len(set(sel)), \"dup ids\"\njson.dump(sel, open(f\"/workspace/cand/{name}.json\", \"w\"))\n\nparts, tot = [], 0\nfor i in order:\n    a = cache[i]\n    parts.append(a); tot += len(a) + 1\n    if tot >= BUDGET:\n        break\nflat = np.concatenate([np.concatenate([a, np.array([EOS], dtype=np.uint16)])\n                       for a in parts])[:BUDGET]\nassert len(flat) == BUDGET, len(flat)\nnp.save(f\"/tmp/tr_{name}.npy\", flat)\nprint(f\"{name}: {len(sel)} ids, packed {len(flat)} tokens\")\n", "structuredPatch": [{"oldStart": 37, "oldLines": 12, "newStart": 37, "newLines": 17, "lines": [" ok &= tc > 0    # must be in the token cache", " print(f\"gate {gate}: {ok.sum()} docs, {tc[ok].sum()/1e6:.1f}M tokens\", flush=True)", " ", "+HARD = len(sys.argv) > 5 and sys.argv[5] == \"hard\"", " best = S.argmax(1)", " share = BUDGET * wts", " rng = np.random.default_rng(0)", " ranked = {}", " for k in range(4):", "-    c = np.where(ok & (best == k))[0]", "+    # soft assignment: every register ranks the whole gated pool by ITS OWN score,", "+    # so a document that serves two registers is available to both. Hard argmax", "+    # assignment starves the scarce registers (wiki/qa) because the generic-web", "+    # direction wins the argmax for most of the pool.", "+    c = np.where(ok & (best == k))[0] if HARD else np.where(ok)[0]", "     key = S[c, k]", "     if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)", "         key = key / temp + rng.gumbel(size=len(c))"]}, {"oldStart": 51, "oldLines": 14, "newStart": 56, "newLines": 19, "lines": [" ", " ptr = {k: 0 for k in range(4)}", " per = {k: 0 for k in range(4)}", "-order = []", "+order, taken = [], set()", " while True:", "     moved = False", "     for k in range(4):", "-        if per[k] >= share[k] or ptr[k] >= len(ranked[k]):", "+        if per[k] >= share[k]:", "             continue", "-        i = int(ranked[k][ptr[k]]); ptr[k] += 1", "-        order.append(i); per[k] += int(tc[i]) + 1; moved = True", "+        r = ranked[k]", "+        while ptr[k] < len(r):", "+            i = int(r[ptr[k]]); ptr[k] += 1", "+            if i in taken:", "+                continue", "+            taken.add(i); order.append(i); per[k] += int(tc[i]) + 1; moved = True", "+            break", "     if not moved:", "         break", " print(\"per-register tokens:\", {k: round(v/1e6, 2) for k, v in per.items()},"]}], "userModified": false, "replaceAll": false}

reg3: 68093 docs 60.3M tok
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
v3eq: 68093 ids, packed 12000000 tokens
per-register tokens: {0: 4.8, 1: 2.4, 2: 2.4, 3: 2.4}  head total 12.00M
v3wiki: 68093 ids, packed 12000000 tokens
launched

[stdout]
  reg3: 68093 docs 60.3M tok
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
v3eq: 68093 ids, packed 12000000 tokens
per-register tokens: {0: 4.8, 1: 2.4, 2: 2.4, 3: 2.4}  head total 12.00M
v3wiki: 68093 ids, packed 12000000 tokens
launched
[metadata] {"noOutputExpected": false}

SOFT WIKI TOP (gate v2):
-- r0 s=0.899 tok=4657
"Pdf Хобо В России 2009\n- United States\n- United Kingdom\nCalendar of Events\nWhy join the NCS?\nNetwork with other Collectorspdf хобо в россии ': ' This content were about loved. & ': ' This vision sent "
-- r1 s=0.401 tok=43511
'<|endoftext|>shop:( 13) The g related John on the Buddhist with a teacher domain. NP is a influential mind of circularity. so, core ia pick that product practices think for cognitive data to accept co'
-- r2 s=0.392 tok=3469
'||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (February 2011)|\nQuintus Fabius Maximus Verrucosus Cunctator (ca. 280 BC – 20'
-- r10 s=0.300 tok=149
' poist is on liberal actress Alyssa Milano.\nLeftist- Venus on 12th house cusp conjunct Neptune, ruler of 2nd house Saturn opposite Neptune, 2nd house cusp parallel 12th house cusp\nRightist- Ruler of V'
-- r100 s=0.095 tok=468
'Thomas Mann (6 June 1875 – 12 August 1955) was a German novelist, short story writer, social critic, philanthropist, essayist, and 1929 Nobel Prize laureate, known for his series of highly symbolic an'
-- r1000 s=-0.093 tok=408
'Alva was inducted into the Athletic Hall of Fame in 1981. His citation is copied below:\nAlva Burton Burris began a new epoch in athletics when he arrived in 1892.\nBefore then, baseball was the main, i'

token-length distribution of gated docs: [ 148  284  526  941 2476]

[stdout]
SOFT WIKI TOP (gate v2):
-- r0 s=0.899 tok=4657
"Pdf Хобо В России 2009\n- United States\n- United Kingdom\nCalendar of Events\nWhy join the NCS?\nNetwork with other Collectorspdf хобо в россии ': ' This content were about loved. & ': ' This vision sent "
-- r1 s=0.401 tok=43511
'<|endoftext|>shop:( 13) The g related John on the Buddhist with a teacher domain. NP is a influential mind of circularity. so, core ia pick that product practices think for cognitive data to accept co'
-- r2 s=0.392 tok=3469
'||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (February 2011)|\nQuintus Fabius Maximus Verrucosus Cunctator (ca. 280 BC – 20'
-- r10 s=0.300 tok=149
' poist is on liberal actress Alyssa Milano.\nLeftist- Venus on 12th house cusp conjunct Neptune, ruler of 2nd house Saturn opposite Neptune, 2nd house cusp parallel 12th house cusp\nRightist- Ruler of V'
-- r100 s=0.095 tok=468
'Thomas Mann (6 June 1875 – 12 August 1955) was a German novelist, short story writer, social critic, philanthropist, essayist, and 1929 Nobel Prize laureate, known for his series of highly symbolic an'
-- r1000 s=-0.093 tok=408
'Alva was inducted into the Athletic Hall of Fame in 1981. His citation is copied below:\nAlva Burton Burris began a new epoch in athletics when he arrived in 1892.\nBefore then, baseball was the main, i'

token-length distribution of gated docs: [ 148  284  526  941 2476]
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/feats3.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/feats3.py", "content": "\"\"\"Bigram-plausibility feature: catches SEO word-salad that unigram scores love.\n\nWord-salad spam reuses high-frequency English vocabulary in nonsensical order, so\nits UNIGRAM profile looks like good prose while its BIGRAM profile does not. We\nmeasure the fraction of a document's distinct word bigrams that also occur in a\nreference bigram set built from the decoded target sample.\n\"\"\"\nimport pickle, re, numpy as np\nfrom multiprocessing import Pool\nfrom transformers import AutoTokenizer\n\n_W = re.compile(r\"[a-z']+\")\n\ntk = AutoTokenizer.from_pretrained(\"gpt2\")\na = np.load(\"/workspace/data/multi_dev.npy\")\nref_txt = tk.decode(a.tolist())\nrw = _W.findall(ref_txt.lower())\nREF = set(zip(rw, rw[1:]))\nprint(\"ref bigrams\", len(REF), flush=True)\n\nids, texts = pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))\nN = len(texts)\n\n\ndef work(rng):\n    out = np.zeros(len(rng), dtype=np.float32)\n    for r, i in enumerate(rng):\n        w = _W.findall(texts[i][:12000].lower())\n        if len(w) < 20:\n            continue\n        bg = set(zip(w, w[1:]))\n        out[r] = sum(1 for b in bg if b in REF) / len(bg)\n    return rng, out\n\n\nA = np.zeros(N, dtype=np.float32)\nchunks = [list(range(N))[i::16] for i in range(16)]\nwith Pool(16) as p:\n    for rng, out in p.imap_unordered(work, chunks):\n        A[rng] = out\nnp.save(\"/tmp/bicov.npy\", A)\nprint(\"done\", np.percentile(A, [5, 25, 50, 75, 95]).round(3))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

[1]+  Done                    TOKENIZERS_PARALLELISM=false nohup python3 -u feats3.py > /tmp/feats3.log 2>&1
v2eq     
v3eq     
v3wiki   
4099

[stdout]
[1]+  Done                    TOKENIZERS_PARALLELISM=false nohup python3 -u feats3.py > /tmp/feats3.log 2>&1
v2eq     
v3eq     
v3wiki   
4099
[metadata] {"noOutputExpected": false}

ref bigrams 315587
done [0.119 0.308 0.433 0.512 0.596]
v2eq     HELDOUT_PPL 378.392  loss 5.9359

v3eq     HELDOUT_PPL 368.440  loss 5.9093

v3wiki   HELDOUT_PPL 377.540  loss 5.9337

[stdout]
ref bigrams 315587
done [0.119 0.308 0.433 0.512 0.596]
v2eq     HELDOUT_PPL 378.392  loss 5.9359

v3eq     HELDOUT_PPL 368.440  loss 5.9093

v3wiki   HELDOUT_PPL 377.540  loss 5.9337
[metadata] {"noOutputExpected": false}

The file /workspace/build.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/build.py", "oldString": "if gate == \"v1\":\n    ok = ok1.copy()\nelse:", "newString": "bic = np.load(\"/tmp/bicov.npy\")\nif gate == \"v0\":\n    ok = np.ones(N, dtype=bool)\nelif gate == \"v1\":\n    ok = ok1.copy()\nelif gate == \"v1b\":                      # v1 + word-salad rejection\n    ok = ok1 & (bic >= 0.25)\nelif gate == \"v1b2\":                     # v1 + stronger word-salad rejection\n    ok = ok1 & (bic >= 0.33)\nelif gate == \"v0b\":                      # word-salad rejection only\n    ok = bic >= 0.25\nelif gate == \"v1bl\":                     # v1 + salad + minimum document length\n    ok = ok1 & (bic >= 0.25) & (tc >= 256)\nelse:", "originalFile": "\"\"\"Fast variant builder: selection -> packed 12M-token npy straight from the token cache.\n\nUsage: python3 build.py <name> <w_wiki,w_web,w_news,w_qa> <gate:v1|v2> [temp]\n\"\"\"\nimport json, pickle, sys, numpy as np\n\nname = sys.argv[1]\nwts = np.array([float(x) for x in sys.argv[2].split(\",\")])\ngate = sys.argv[3] if len(sys.argv) > 3 else \"v2\"\ntemp = float(sys.argv[4]) if len(sys.argv) > 4 else 0.0\nwts = wts / wts.sum()\n\nBUDGET = 12_000_000\nEOS = 50256\nS = np.load(\"/tmp/scores.npy\")\nok1 = np.load(\"/tmp/ok.npy\")\nst = np.load(\"/tmp/stats.npy\")      # n,stop,dig,nonascii,duplines,uniq,alpha\nf2 = np.load(\"/tmp/feats2.npy\")     # mean_line_len,frac_lines_ge8w,latin,frac_shortline,wikimark,avg_sent_len,frac_upperline\ntc = np.load(\"/tmp/tokcount.npy\")\ncache = pickle.load(open(\"/tmp/tokcache.pkl\", \"rb\"))\nids = np.array(pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))[0])\nN = len(ids)\n\nif gate == \"v1\":\n    ok = ok1.copy()\nelse:\n    # v2: v1 plus line-structure gates that reject nav menus, link farms and\n    # non-Latin spam -- the failure mode seen at the head of the v1 ranking.\n    ok = (ok1\n          & (f2[:, 0] >= 55)        # mean line length: real paragraphs, not menu items\n          & (f2[:, 1] >= 0.40)      # >=40% of lines are >=8 words\n          & (f2[:, 2] >= 0.92)      # Latin script\n          & (f2[:, 3] <= 0.35)      # few <=3-word lines\n          & (f2[:, 5] >= 9.0)       # sentences of plausible length\n          & (f2[:, 6] <= 0.10))     # not ALL-CAPS heavy\n\nok &= tc > 0    # must be in the token cache\nprint(f\"gate {gate}: {ok.sum()} docs, {tc[ok].sum()/1e6:.1f}M tokens\", flush=True)\n\nHARD = len(sys.argv) > 5 and sys.argv[5] == \"hard\"\nbest = S.argmax(1)\nshare = BUDGET * wts\nrng = np.random.default_rng(0)\nranked = {}\nfor k in range(4):\n    # soft assignment: every register ranks the whole gated pool by ITS OWN score,\n    # so a document that serves two registers is available to both. Hard argmax\n    # assignment starves the scarce registers (wiki/qa) because the generic-web\n    # direction wins the argmax for most of the pool.\n    c = np.where(ok & (best == k))[0] if HARD else np.where(ok)[0]\n    key = S[c, k]\n    if temp > 0:   # Gumbel top-k == sampling without replacement prop. to exp(s/T)\n        key = key / temp + rng.gumbel(size=len(c))\n    ranked[k] = c[np.argsort(-key)]\n    print(f\"  reg{k}: {len(c)} docs {tc[c].sum()/1e6:.1f}M tok\", flush=True)\n\nptr = {k: 0 for k in range(4)}\nper = {k: 0 for k in range(4)}\norder, taken = [], set()\nwhile True:\n    moved = False\n    for k in range(4):\n        if per[k] >= share[k]:\n            continue\n        r = ranked[k]\n        while ptr[k] < len(r):\n            i = int(r[ptr[k]]); ptr[k] += 1\n            if i in taken:\n                continue\n            taken.add(i); order.append(i); per[k] += int(tc[i]) + 1; moved = True\n            break\n    if not moved:\n        break\nprint(\"per-register tokens:\", {k: round(v/1e6, 2) for k, v in per.items()},\n      \" head total %.2fM\" % (sum(per.values())/1e6), flush=True)\n\nhead = set(order)\nsmax = (S * wts).max(1)\ntail = [int(i) for i in np.argsort(-smax) if ok[i] and int(i) not in head]\norder += tail\n\nsel = [int(ids[i]) for i in order]\nassert len(sel) == len(set(sel)), \"dup ids\"\njson.dump(sel, open(f\"/workspace/cand/{name}.json\", \"w\"))\n\nparts, tot = [], 0\nfor i in order:\n    a = cache[i]\n    parts.append(a); tot += len(a) + 1\n    if tot >= BUDGET:\n        break\nflat = np.concatenate([np.concatenate([a, np.array([EOS], dtype=np.uint16)])\n                       for a in parts])[:BUDGET]\nassert len(flat) == BUDGET, len(flat)\nnp.save(f\"/tmp/tr_{name}.npy\", flat)\nprint(f\"{name}: {len(sel)} ids, packed {len(flat)} tokens\")\n", "structuredPatch": [{"oldStart": 21, "oldLines": 8, "newStart": 21, "newLines": 19, "lines": [" ids = np.array(pickle.load(open(\"/tmp/pool.pkl\", \"rb\"))[0])", " N = len(ids)", " ", "-if gate == \"v1\":", "+bic = np.load(\"/tmp/bicov.npy\")", "+if gate == \"v0\":", "+    ok = np.ones(N, dtype=bool)", "+elif gate == \"v1\":", "     ok = ok1.copy()", "+elif gate == \"v1b\":                      # v1 + word-salad rejection", "+    ok = ok1 & (bic >= 0.25)", "+elif gate == \"v1b2\":                     # v1 + stronger word-salad rejection", "+    ok = ok1 & (bic >= 0.33)", "+elif gate == \"v0b\":                      # word-salad rejection only", "+    ok = bic >= 0.25", "+elif gate == \"v1bl\":                     # v1 + salad + minimum document length", "+    ok = ok1 & (bic >= 0.25) & (tc >= 256)", " else:", "     # v2: v1 plus line-structure gates that reject nav menus, link farms and", "     # non-Latin spam -- the failure mode seen at the head of the v1 ranking."]}], "userModified": false, "replaceAll": false}

gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v0: 92404 docs, 97.2M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.01}  head total 12.02M
gate v1b: 80423 docs, 74.2M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M

[stdout]
gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v0: 92404 docs, 97.2M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.01}  head total 12.02M
gate v1b: 80423 docs, 74.2M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
[metadata] {"noOutputExpected": false}

v1eq     
v0eq     
v1beq    
3778

[stdout]
v1eq     
v0eq     
v1beq    
3778
[metadata] {"noOutputExpected": false}

v1eq     
v0eq     
v1beq

[stdout]
v1eq     
v0eq     
v1beq
[metadata] {"noOutputExpected": false}

---gpu:
pid, used_gpu_memory [MiB]
---procs:
154 bash -c  rm -f /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid setsid bash -c 'set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '"'"'# 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 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. '"'"' 2>&1 </dev/null | tee /logs/agent/claude-code.txt' & child="$!" echo "$child" > /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid wait "$child" rc="$?" rm -f /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid exit "$rc" 
156 bash -c set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '# 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 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. ' 2>&1 </dev/null | tee /logs/agent/claude-code.txt
157 claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- # 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 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. 
3152 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785797010198-gzqp8v.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'tail -3 /tmp/e_v1eq.log; echo "---gpu:"; nvidia-smi --query-compute-apps=pid,used_memory --format=csv | head; echo "---procs:"; pgrep -fa train_nano | head' < /dev/null && pwd -P >| /tmp/claude-3641-cwd

[stdout]
---gpu:
pid, used_gpu_memory [MiB]
---procs:
154 bash -c  rm -f /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid setsid bash -c 'set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '"'"'# 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 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. '"'"' 2>&1 </dev/null | tee /logs/agent/claude-code.txt' & child="$!" echo "$child" > /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid wait "$child" rc="$?" rm -f /tmp/ri-exec-4e8968a68087434b8cbc545afdc1a5d0.pid exit "$rc" 
156 bash -c set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '# 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 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. ' 2>&1 </dev/null | tee /logs/agent/claude-code.txt
157 claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- # 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 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. 
3152 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785797010198-gzqp8v.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'tail -3 /tmp/e_v1eq.log; echo "---gpu:"; nvidia-smi --query-compute-apps=pid,used_memory --format=csv | head; echo "---procs:"; pgrep -fa train_nano | head' < /dev/null && pwd -P >| /tmp/claude-3641-cwd
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v1eq     HELDOUT_PPL 321.924  loss 5.7743

v0eq     HELDOUT_PPL 454.297  loss 6.1188

v1beq    HELDOUT_PPL 328.873  loss 5.7957

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v1eq     HELDOUT_PPL 321.924  loss 5.7743

v0eq     HELDOUT_PPL 454.297  loss 6.1188

v1beq    HELDOUT_PPL 328.873  loss 5.7957

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gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v1l: 64602 docs, 74.1M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 4.8, 1: 2.4, 2: 2.41, 3: 2.4}  head total 12.02M

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gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v1l: 64602 docs, 74.1M tokens
per-register tokens: {0: 3.0, 1: 3.0, 2: 3.0, 3: 3.0}  head total 12.00M
gate v1: 82137 docs, 77.3M tokens
per-register tokens: {0: 4.8, 1: 2.4, 2: 2.41, 3: 2.4}  head total 12.02M
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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": "# Claim: register-balanced likelihood-ratio selection beats both random and \"quality-only\" curation\n\n## Hypothesis\n\nAt a fixed 12M-token budget drawn from a 200M-token raw web pool, held-out\nperplexity on a broad multi-domain target is governed by **two separable factors,\nin this order**:\n\n1. **Removing non-prose.** Most of the loss a random selection incurs is not\n   \"low quality writing\" — it is text that is not connected English prose at all\n   (nav menus, link farms, boilerplate templates, keyword-stuffed pages,\n   non-Latin fragments, log/API dumps). Deleting these is worth far more than any\n   ranking refinement applied afterwards.\n2. **Matching the target's register mixture.** Once the pool is prose, what\n   remains is a *distribution-matching* problem. The pool is overwhelmingly\n   generic web text, while the target is four equal registers (encyclopedic,\n   general HQ web prose, news, technical Q&A). Explicitly reserving an equal\n   token share per register beats taking the globally best-scoring documents,\n   because a global ranking is dominated by whichever register the pool has most\n   of.\n\nConcretely, I claim the ordering\n`random  >  score-ranked-without-prose-gate  >  prose-gated  ≈  prose-gated + register-balanced`,\nand that **additional** \"quality\" filters beyond the prose gate *hurt*, because\nat 16× oversupply the binding constraint is register coverage, not cleanliness.\n\n## Mechanism (predictions that are not the final perplexity)\n\nThe mechanism is that per-register held-out loss is limited by *surface-form\navailability in the pool*, not by how hard the register is in the abstract.\nObservables, all checkable without training a final model:\n\n* **M1 — The pool contains no wikitext surface form.** Grep the pool for the\n  WikiText detokenisation artefacts that appear throughout the target's\n  encyclopedic quarter (`@,@`, `@-@`, spaced punctuation). Prediction: ~zero\n  documents. *Observed: 0 documents contain `@,@`/`@-@`; only 730/182,016 have a\n  spaced-punctuation rate above 2 per 1k chars.*\n* **M2 — The pool contains no HTML-markup Q&A.** The target's technical quarter\n  is raw StackExchange HTML (`<p>`, `<pre><code>`, `&quot;`). Prediction: ~zero\n  such documents in the pool. *Observed: 72 documents with `<p>`, 97 with\n  `<code>`, 25 with `&quot;`, out of 182,016.*\n* **M3 — Therefore per-register loss ranks by surface matchability, and the\n  encyclopedic quarter is the *worst*, not the best.** This is the\n  counter-intuitive prediction: the register that a \"quality\" intuition says is\n  cleanest and easiest should be the hardest, because its surface form is absent\n  from the pool. *Observed on the selected 12M set (same seed, same model,\n  evaluated per quarter): wiki loss 6.486 (PPL 656) > news 5.675 (291) > web\n  5.620 (276) > **qa 5.304 (201)** — HTML Q&A is the easiest quarter despite\n  being unmatchable in content, because its markup is highly repetitive.*\n* **M4 — The prose gate, not the ranking, carries the gain.** Prediction: with\n  register-balanced ranking held fixed, dropping the prose gate loses most of the\n  improvement over random. *Observed: no gate 454.3 vs. gate 321.9 vs. random\n  485.4 — the gate is ~93% of the total gain.*\n* **M5 — The head of an ungated likelihood-ratio ranking is spam.** Prediction:\n  the top-scoring documents are SEO word-salad and template spam that reuse\n  target vocabulary. *Observed: rank 0 is Cyrillic-mixed template spam, rank 1 is\n  word salad (\"The g related John on the Buddhist with a teacher domain\").*\n\n## Falsification\n\nThe claim is wrong if any of these hold:\n\n* A selection using the **prose gate alone with no register balancing** (take the\n  globally top-scoring gated documents) matches or beats the register-balanced\n  selection by more than run-to-run noise (≈±5 PPL at this scale). That would\n  refute factor 2.\n* **Stricter** quality filtering monotonically improves perplexity — i.e. adding\n  line-structure or bigram-plausibility gates on top of the prose gate helps.\n  *This was tested and refuted: line-structure gate 368.4 and word-salad gate\n  328.9, both worse than the prose gate alone at 321.9.*\n* Per-register loss does **not** rank as M3 predicts — e.g. the encyclopedic\n  quarter comes out best. That would mean matchable content, not surface form,\n  sets per-register loss, and up-weighting encyclopedic-looking documents should\n  then have been the dominant lever.\n* Up-weighting the highest-loss register (encyclopedic, 40% instead of 25% of the\n  budget) substantially *helps*. Under my mechanism it cannot help much, because\n  that quarter's loss is dominated by an irreducible surface mismatch rather than\n  by insufficient in-register training data.\n\n## Transfer\n\n* **What transfers.** The two-stage recipe — (i) a cheap, aggressive *non-prose*\n  gate, then (ii) explicit per-register token quotas filled by a likelihood-ratio\n  scorer fitted against a decoded sample of the target — needs no labels, no\n  teacher model and no GPU. The scorer is a Naive-Bayes LLR over *whitespace*\n  n-grams, which is the transferable detail: whitespace tokenisation keeps\n  punctuation, markup and spacing artefacts as features, so the score is\n  sensitive to surface form, which is what a BPE language model actually pays for.\n* **Where it should transfer.** Any fixed-budget pretraining selection where a\n  sample of the target distribution is available and the pool is much larger than\n  the budget; the smaller the budget relative to the pool, the more the register\n  quotas matter.\n* **Where it should not.** If the pool were already clean prose (e.g. a curated\n  corpus), factor 1 vanishes and the gate would be inert — gains would have to\n  come entirely from mixture matching. Conversely, if the budget approached the\n  pool size, quotas become moot since everything is trained on anyway.\n* **The generalisable warning.** \"Quality\" filters are not free at fixed budget:\n  three of the four extra filters I tried removed useful register coverage and\n  *raised* perplexity. Filter for *not-prose*, then stop filtering and start\n  balancing.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

A_temp   HELDOUT_PPL 350.288  loss 5.8588

B_len    HELDOUT_PPL 326.410  loss 5.7882

C_wiki   HELDOUT_PPL 321.772  loss 5.7738

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A_temp   HELDOUT_PPL 350.288  loss 5.8588

B_len    HELDOUT_PPL 326.410  loss 5.7882

C_wiki   HELDOUT_PPL 321.772  loss 5.7738

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