claude-opus-4-8

@      0 wiki=  5 code=  0 |  Beardmore was sufficiently impressed with Shackleton to off
@  40000 wiki=  4 code=  0 |  " , such as Joe DiMaggio ( Italian @-@ American ) and Arthu
@  80000 wiki=  0 code=  0 |  behalf of South Korea . The United States , a member of the
@ 120000 wiki=  8 code=  0 |  , so named because it was once a rail line into downtown Ne
@ 160000 wiki=  2 code=  0 |  ) .  <|endoftext|> The December 2000 nor 'easter was a sign
@ 200000 wiki=  9 code=  0 |  it followed the southern end of the Lodge Freeway . By the 
@ 240000 wiki=  7 code=  0 | aics differ across countries , including Australia , China ,
@ 280000 wiki=  0 code=  0 | �t even know how to be there.  They don’t know how to promot
@ 320000 wiki=  0 code=  0 | . EDD is calculated using Parikh's formula by adding nine mo
@ 360000 wiki=  0 code=  0 |  Godfrey and Seth Scarborough  Share this post<|endoftext|>T
@ 400000 wiki=  0 code=  0 |  bite out of Walker's counting stats. He retired with 2,160 
@ 440000 wiki=  0 code=  0 |  how new motherhood really can be. To let her in on all the 
@ 480000 wiki=  0 code=  0 | ness, fairness and accuracy.”  Some frustrations with Baker’
@ 520000 wiki=  0 code=  0 |  in Etah and Jaithra town Yadav alleged that BJP has "copied
@ 560000 wiki=  0 code=  0 |  next round of examination. It also contained the names of f
@ 600000 wiki=  0 code=  0 |  flexibility to employees and saves seating space for the em
@ 640000 wiki=  0 code=  0 |  to the Lieutenant Governor”, “forwarded to the Lieutenant G
@ 680000 wiki=  0 code=  0 |  Elgar, Shorter in length around middle, Elgar ducks under i
@ 720000 wiki=  0 code=  0 |  media to report cases of sexual offences against child vict
@ 760000 wiki=  0 code= 11 |  echo "Line 0: '${LINES[0]}'"     echo "Line 1: '${LINES[1]}
@ 800000 wiki=  0 code= 35 |  true         };         client.Send(&quot;MyEmailAddress@gm
@ 840000 wiki=  0 code=  8 |  recreated   from other resources. It is   commonplace to ex
@ 880000 wiki=  0 code= 14 |  at my code and suggest how can I resolve this error.</p>  <
@ 920000 wiki=  0 code= 15 | >. And it's not documented there, so it probably doesn't exi
@ 960000 wiki=  0 code=  9 | -php/1301012#1301012">Quick question about sessions in PHP</
===timer===
4278

[stdout]
@      0 wiki=  5 code=  0 |  Beardmore was sufficiently impressed with Shackleton to off
@  40000 wiki=  4 code=  0 |  " , such as Joe DiMaggio ( Italian @-@ American ) and Arthu
@  80000 wiki=  0 code=  0 |  behalf of South Korea . The United States , a member of the
@ 120000 wiki=  8 code=  0 |  , so named because it was once a rail line into downtown Ne
@ 160000 wiki=  2 code=  0 |  ) .  <|endoftext|> The December 2000 nor 'easter was a sign
@ 200000 wiki=  9 code=  0 |  it followed the southern end of the Lodge Freeway . By the 
@ 240000 wiki=  7 code=  0 | aics differ across countries , including Australia , China ,
@ 280000 wiki=  0 code=  0 | �t even know how to be there.  They don’t know how to promot
@ 320000 wiki=  0 code=  0 | . EDD is calculated using Parikh's formula by adding nine mo
@ 360000 wiki=  0 code=  0 |  Godfrey and Seth Scarborough  Share this post<|endoftext|>T
@ 400000 wiki=  0 code=  0 |  bite out of Walker's counting stats. He retired with 2,160 
@ 440000 wiki=  0 code=  0 |  how new motherhood really can be. To let her in on all the 
@ 480000 wiki=  0 code=  0 | ness, fairness and accuracy.”  Some frustrations with Baker’
@ 520000 wiki=  0 code=  0 |  in Etah and Jaithra town Yadav alleged that BJP has "copied
@ 560000 wiki=  0 code=  0 |  next round of examination. It also contained the names of f
@ 600000 wiki=  0 code=  0 |  flexibility to employees and saves seating space for the em
@ 640000 wiki=  0 code=  0 |  to the Lieutenant Governor”, “forwarded to the Lieutenant G
@ 680000 wiki=  0 code=  0 |  Elgar, Shorter in length around middle, Elgar ducks under i
@ 720000 wiki=  0 code=  0 |  media to report cases of sexual offences against child vict
@ 760000 wiki=  0 code= 11 |  echo "Line 0: '${LINES[0]}'"     echo "Line 1: '${LINES[1]}
@ 800000 wiki=  0 code= 35 |  true         };         client.Send(&quot;MyEmailAddress@gm
@ 840000 wiki=  0 code=  8 |  recreated   from other resources. It is   commonplace to ex
@ 880000 wiki=  0 code= 14 |  at my code and suggest how can I resolve this error.</p>  <
@ 920000 wiki=  0 code= 15 | >. And it's not documented there, so it probably doesn't exi
@ 960000 wiki=  0 code=  9 | -php/1301012#1301012">Quick question about sessions in PHP</
===timer===
4278
[metadata] {"noOutputExpected": false}

backed up unigram selection
-rw-r--r-- 1 root root   5825494 Aug  4 04:36 /tmp/feats.npz
-rw-r--r-- 1 root root 402736548 Aug  4 04:34 /tmp/pool_tok.npz

[stdout]
backed up unigram selection
-rw-r--r-- 1 root root   5825494 Aug  4 04:36 /tmp/feats.npz
-rw-r--r-- 1 root root 402736548 Aug  4 04:34 /tmp/pool_tok.npz
[metadata] {"noOutputExpected": false}

File created successfully at: /tmp/rank3.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/rank3.py", "content": "import json, time, numpy as np\nt0=time.time()\nz=np.load(\"/tmp/pool_tok.npz\"); flat,offs,ids=z[\"flat\"],z[\"offs\"],z[\"ids\"]\nf=np.load(\"/tmp/feats.npz\"); ttr,top1=f[\"ttr\"],f[\"top1\"]\nV=50257; ALPHA=0.5\nflat64=flat.astype(np.int64)\ntgt=np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64)\ncq=np.bincount(flat64,minlength=V).astype(np.float64)\npq=(cq+ALPHA)/(cq.sum()+ALPHA*V); logpq=np.log(pq)\n# register slices (token-index ranges), gaps skip transition zones\nREG={\"wiki\":(0,260000),\"news\":(280000,750000),\"qa\":(760000,1000000)}\nntok=offs[1:]-offs[:-1]\nscore={}\nfor r,(a,b) in REG.items():\n    c=np.bincount(tgt[a:b],minlength=V).astype(np.float64)\n    p=(c+ALPHA)/(c.sum()+ALPHA*V)\n    w=np.log(p)-logpq\n    wf=w[flat64]; wc=np.zeros(len(flat)+1); np.cumsum(wf,out=wc[1:])\n    score[r]=(wc[offs[1:]]-wc[offs[:-1]])/np.maximum(ntok,1)\n# combined (for tail ordering)\ncc=np.bincount(tgt,minlength=V).astype(np.float64)\npcomb=(cc+ALPHA)/(cc.sum()+ALPHA*V); wcomb=np.log(pcomb)-logpq\nwf=wcomb[flat64]; wc=np.zeros(len(flat)+1); np.cumsum(wf,out=wc[1:])\nscore[\"comb\"]=(wc[offs[1:]]-wc[offs[:-1]])/np.maximum(ntok,1)\nprint(f\"[{time.time()-t0:.0f}s] scored 3 registers\")\n# dedup\nseen=set(); dup=np.zeros(len(ids),dtype=bool)\nfor i in range(len(ids)):\n    h=hash(flat[offs[i]:offs[i+1]].tobytes())\n    if h in seen: dup[i]=True\n    else: seen.add(h)\nvalid=(ntok>=50)&(ntok<=20000)&(top1<=0.30)&(ttr>=0.30)&(~dup)\nvidx=np.where(valid)[0]\norder_r={r:vidx[np.argsort(-score[r][vidx])] for r in REG}\n# balanced round-robin fill: wiki:news:qa = 1:2:1  (news block ~= web+news)\nbudgets={\"wiki\":3_000_000,\"news\":6_000_000,\"qa\":3_000_000}\npattern=[\"wiki\",\"news\",\"qa\",\"news\"]\nptr={r:0 for r in REG}; used={r:0 for r in REG}; taken=set(); final=[]; acc=0\nwhile acc<12_000_000:\n    prog=False\n    for r in pattern:\n        if used[r]>=budgets[r]: continue\n        lst=order_r[r]\n        while ptr[r]<len(lst) and (lst[ptr[r]] in taken): ptr[r]+=1\n        if ptr[r]>=len(lst): continue\n        d=int(lst[ptr[r]]); ptr[r]+=1; taken.add(d)\n        final.append(d); n=int(ntok[d])+1; used[r]+=n; acc+=n; prog=True\n    if not prog: break\nprint(f\"[{time.time()-t0:.0f}s] balanced fill: {len(final)} docs, {acc} tok; \"\n      f\"used wiki={used['wiki']} news={used['news']} qa={used['qa']}\")\n# append remaining valid by combined score, then junk\nrest=[i for i in vidx[np.argsort(-score['comb'][vidx])] if int(i) not in taken]\njunk=list(np.where(~valid)[0])\nallidx=final+[int(i) for i in rest]+[int(i) for i in junk]\nsel=[int(ids[i]) for i in allidx]\njson.dump(sel, open(\"/tmp/sel_bal.json\",\"w\"))\nprint(f\"[{time.time()-t0:.0f}s] wrote /tmp/sel_bal.json ({len(sel)} ids)\")\n# quick composition peek\ntext={}\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r=json.loads(line); text[r[\"id\"]]=r[\"text\"]\nfor r in REG:\n    top=order_r[r][:3]\n    print(f\"--- top {r} ---\")\n    for i in top: print(\"   \"+repr(text[int(ids[i])][:110]))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

[9s] scored 3 registers
[10s] balanced fill: 19938 docs, 12000382 tok; used wiki=3000020 news=6001987 qa=2998375
[10s] wrote /tmp/sel_bal.json (182016 ids)
--- top wiki ---
   ' Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . '
   'omaya Hendousa founded dumped in slums east of Khartoum , severely tortured and her hair shaved confirmed reso'
   ' Yahoo Beauty.<|endoftext|>2007-2008: Enters her junior year fully healed from a knee injury suffered her seni'
--- top news ---
   ' to local government officials, the incident took place around 9:30 pm local time leaving the district police '
   'The Director General of Military Operations for the Pakistani Army spoke with his Indian counterpart via telep'
   'Prime Minister Imran Khan on Thursday congratulated India’s premier Narendra Modi on his victory in the countr'
--- top qa ---
   'XForms/Read and write with get and put\nSometimes all you need to do is to put a nice user friendly form that e'
   '!<|endoftext|>www.3gpp.org - /ftp/tsg_ct/tsg_ct/TSGC_69_Phoenix/\nwww.3gpp.org - /ftp/tsg_ct/tsg_ct/TSGC_69_Pho'
   ' can anybody tell me how to create a file and write on it on the URl.\nActually I am using an applet and from t'
===timer===
4198

[stdout]
[9s] scored 3 registers
[10s] balanced fill: 19938 docs, 12000382 tok; used wiki=3000020 news=6001987 qa=2998375
[10s] wrote /tmp/sel_bal.json (182016 ids)
--- top wiki ---
   ' Saddleback College in Mission Viejo, Calif. . . . Was an all-conference pick a year ago on the JCO level . . '
   'omaya Hendousa founded dumped in slums east of Khartoum , severely tortured and her hair shaved confirmed reso'
   ' Yahoo Beauty.<|endoftext|>2007-2008: Enters her junior year fully healed from a knee injury suffered her seni'
--- top news ---
   ' to local government officials, the incident took place around 9:30 pm local time leaving the district police '
   'The Director General of Military Operations for the Pakistani Army spoke with his Indian counterpart via telep'
   'Prime Minister Imran Khan on Thursday congratulated India’s premier Narendra Modi on his victory in the countr'
--- top qa ---
   'XForms/Read and write with get and put\nSometimes all you need to do is to put a nice user friendly form that e'
   '!<|endoftext|>www.3gpp.org - /ftp/tsg_ct/tsg_ct/TSGC_69_Phoenix/\nwww.3gpp.org - /ftp/tsg_ct/tsg_ct/TSGC_69_Pho'
   ' can anybody tell me how to create a file and write on it on the URl.\nActually I am using an applet and from t'
===timer===
4198
[metadata] {"noOutputExpected": false}

pid 1995
[1]+  Done                    nohup bash dev_eval.sh /tmp/sel_bal.json > /tmp/eval_bal.log 2>&1
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 323.414  loss 5.7789  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
4078

[stdout]
pid 1995
[1]+  Done                    nohup bash dev_eval.sh /tmp/sel_bal.json > /tmp/eval_bal.log 2>&1
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 323.414  loss 5.7789  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
4078
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "update", "filePath": "/workspace/curate.py", "content": "\"\"\"Curate a raw web pool into a priority-ordered selection for training a small LM.\n\nSTATED CRITERION (reproducible; no hand-picked ids)\n---------------------------------------------------\nThe scoring target is a BROAD, multi-domain high-quality English corpus made of\nfour equal registers: encyclopedic (Wikipedia), high-quality web prose, news, and\ntechnical Q&A. Inspecting the provided dev target (data/multi_dev.npy) shows it is\nlaid out in three contiguous register blocks:\n    Wikipedia   tokens      0 .. 260k   (~25%)\n    web + news  tokens   280k .. 750k   (~50%)\n    technical QA tokens  760k .. 1000k  (~25%)\n\nWe select documents by DSIR-style n-gram importance weighting (Xie et al. 2023),\napplied *per register* and then balanced to the target's register proportions:\n\n  1. Estimate a smoothed unigram model p_r for each register r and a background\n     unigram model q over the whole pool.\n  2. Score every pool doc for register r by the mean per-token log importance\n     weight   s_r(d) = mean_{t in d} [ log p_r(t) - log q(t) ]  -- how much more\n     \"register-r-like\" than \"generic-pool-like\" its tokens are.\n  3. Drop web junk first: too short/long, exact duplicates, and low-diversity /\n     whitespace-dominated boilerplate (one token > 30% of the doc, or type/token\n     ratio < 0.30) -- these otherwise win the raw DSIR score (Apache \"Index of /\"\n     listings, nav menus).\n  4. Fill the 12M-token budget by round-robin across registers in the target's\n     proportion (wiki:web+news:qa = 1:2:1), taking each register's best unused\n     docs. This guarantees the code-heavy QA register (which the *combined*-target\n     unigram starves, since its tokens are rare in the 75%-non-QA target) gets its\n     fair share -- the single biggest driver of held-out perplexity here.\n\nMatching the training MIXTURE to the eval mixture (not memorizing the dev sample)\nis what transfers to the hidden, disjoint official target of the same domain.\n\"\"\"\nimport json, os, sys, time, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL   = \"/workspace/data/pool.jsonl\"\nTARGET = \"/workspace/data/multi_dev.npy\"\nOUT    = \"/workspace/submission/selection.json\"\nCACHE  = \"/tmp/pool_tok.npz\"\nV      = 50257\nALPHA  = 0.5          # add-alpha smoothing for the unigram models\nMINTOK, MAXTOK = 50, 20000\nTOP1MAX, TTRMIN = 0.30, 0.30\nBUDGET = 12_000_000\n# register token-index ranges in the dev target; gaps skip transition zones\nREG    = {\"wiki\": (0, 260_000), \"web_news\": (280_000, 750_000), \"qa\": (760_000, 1_000_000)}\n# budget split proportional to the eval mixture (web_news block = 2 registers)\nREG_BUDGET = {\"wiki\": 3_000_000, \"web_news\": 6_000_000, \"qa\": 3_000_000}\nPATTERN = [\"wiki\", \"web_news\", \"qa\", \"web_news\"]\nt0 = time.time()\n\n# ---------------------------------------------------------------- tokenize pool\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nif os.path.exists(CACHE):\n    z = np.load(CACHE); flat, offs, ids = z[\"flat\"], z[\"offs\"], z[\"ids\"]\n    print(f\"[{time.time()-t0:.0f}s] loaded cache: {len(ids)} docs, {len(flat)} tokens\")\nelse:\n    texts, ids = [], []\n    for line in open(POOL):\n        r = json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\n    ids = np.array(ids, dtype=np.int64)\n    parts, lens = [], np.empty(len(texts), dtype=np.int64)\n    for s in range(0, len(texts), 4000):\n        for j, t in enumerate(tok(texts[s:s+4000], add_special_tokens=False)[\"input_ids\"]):\n            lens[s+j] = len(t); parts.append(np.asarray(t, dtype=np.uint16))\n    flat = np.concatenate(parts)\n    offs = np.zeros(len(texts)+1, dtype=np.int64); offs[1:] = np.cumsum(lens)\n    np.savez(CACHE, flat=flat, offs=offs, ids=ids)\n    print(f\"[{time.time()-t0:.0f}s] tokenized: {len(ids)} docs, {len(flat)} tokens\")\n\nflat64 = flat.astype(np.int64)\nntok   = offs[1:] - offs[:-1]\n\n# --------------------------------------------------- background + register PMFs\ncq = np.bincount(flat64, minlength=V).astype(np.float64)\nlogpq = np.log((cq + ALPHA) / (cq.sum() + ALPHA * V))\ntgt = np.load(TARGET).astype(np.int64)\n\ndef mean_importance(logp):\n    w  = logp - logpq\n    wf = w[flat64]\n    wc = np.zeros(len(flat) + 1); np.cumsum(wf, out=wc[1:])\n    return (wc[offs[1:]] - wc[offs[:-1]]) / np.maximum(ntok, 1)\n\nscore = {}\nfor r, (a, b) in REG.items():\n    c = np.bincount(tgt[a:b], minlength=V).astype(np.float64)\n    score[r] = mean_importance(np.log((c + ALPHA) / (c.sum() + ALPHA * V)))\ncc = np.bincount(tgt, minlength=V).astype(np.float64)\nscore[\"comb\"] = mean_importance(np.log((cc + ALPHA) / (cc.sum() + ALPHA * V)))\nprint(f\"[{time.time()-t0:.0f}s] scored {len(REG)} registers + combined\")\n\n# ------------------------------------------- junk filter (dedup + diversity)\nseen = set(); dup = np.zeros(len(ids), dtype=bool)\nttr  = np.zeros(len(ids)); top1 = np.zeros(len(ids))\nfor i in range(len(ids)):\n    s = flat[offs[i]:offs[i+1]]\n    h = hash(s.tobytes())\n    if h in seen: dup[i] = True\n    else: seen.add(h)\n    if len(s):\n        u, c = np.unique(s, return_counts=True)\n        ttr[i] = len(u) / len(s); top1[i] = c.max() / len(s)\nvalid = ((ntok >= MINTOK) & (ntok <= MAXTOK) & (~dup)\n         & (top1 <= TOP1MAX) & (ttr >= TTRMIN))\nvidx = np.where(valid)[0]\nprint(f\"[{time.time()-t0:.0f}s] valid {valid.sum()}/{len(ids)} \"\n      f\"(dup={int(dup.sum())} lowdiv={int(((top1>TOP1MAX)|(ttr<TTRMIN)).sum())})\")\n\n# ---------------------------------- balanced round-robin fill of the budget\norder_r = {r: vidx[np.argsort(-score[r][vidx])] for r in REG}\nptr = {r: 0 for r in REG}; used = {r: 0 for r in REG}; taken = set(); final = []; acc = 0\nwhile acc < BUDGET:\n    prog = False\n    for r in PATTERN:\n        if used[r] >= REG_BUDGET[r]: continue\n        lst = order_r[r]\n        while ptr[r] < len(lst) and int(lst[ptr[r]]) in taken: ptr[r] += 1\n        if ptr[r] >= len(lst): continue\n        d = int(lst[ptr[r]]); ptr[r] += 1; taken.add(d)\n        final.append(d); n = int(ntok[d]) + 1; used[r] += n; acc += n; prog = True\n    if not prog: break\nprint(f\"[{time.time()-t0:.0f}s] balanced fill: {len(final)} docs {acc} tok \"\n      f\"(wiki={used['wiki']} web_news={used['web_news']} qa={used['qa']})\")\n\n# ------------------ append the rest (combined score) then junk, write output\nrest = [int(i) for i in vidx[np.argsort(-score[\"comb\"][vidx])] if int(i) not in taken]\njunk = [int(i) for i in np.where(~valid)[0]]\nsel  = [int(ids[i]) for i in (final + rest + junk)]\nassert len(sel) == len(set(sel)) == len(ids)          # unique, complete\nos.makedirs(os.path.dirname(OUT), exist_ok=True)\njson.dump(sel, open(OUT, \"w\"))\nprint(f\"[{time.time()-t0:.0f}s] wrote {len(sel)} ids -> {OUT}\")\n", "structuredPatch": [{"oldStart": 1, "oldLines": 23, "newStart": 1, "newLines": 35, "lines": [" \"\"\"Curate a raw web pool into a priority-ordered selection for training a small LM.", " ", "-Criterion (stated, reproducible):", "-  Select documents whose GPT-2 token distribution best matches a disclosed", "-  high-quality, multi-domain English TARGET (Wikipedia + news + high-quality web", "-  prose + technical Q&A), after removing obvious web junk (too short, near-empty,", "-  exact duplicates).", "+STATED CRITERION (reproducible; no hand-picked ids)", "+---------------------------------------------------", "+The scoring target is a BROAD, multi-domain high-quality English corpus made of", "+four equal registers: encyclopedic (Wikipedia), high-quality web prose, news, and", "+technical Q&A. Inspecting the provided dev target (data/multi_dev.npy) shows it is", "+laid out in three contiguous register blocks:", "+    Wikipedia   tokens      0 .. 260k   (~25%)", "+    web + news  tokens   280k .. 750k   (~50%)", "+    technical QA tokens  760k .. 1000k  (~25%)", " ", "-  Quality signal = DSIR-style unigram importance weight (Xie et al. 2023):", "-  for each vocabulary token v,", "-        w[v] = log p_target(v) - log p_pool(v)", "-  (add-alpha smoothed). A document's score is the mean of w over its tokens ---", "-  i.e. how much more \"target-like\" than \"generic-pool-like\" its words are.", "-  Documents are emitted best-first; the training pipeline consumes them in order", "-  until the 12M-token budget is filled.", "+We select documents by DSIR-style n-gram importance weighting (Xie et al. 2023),", "+applied *per register* and then balanced to the target's register proportions:", " ", "-The TARGET distribution is estimated from the provided dev target", "-(data/multi_dev.npy), which is a sample of the disclosed HQ domain. The official", "-scoring target is a *disjoint* sample of the same domain, so matching the domain", "-n-gram statistics (not memorizing the dev set) is what transfers.", "+  1. Estimate a smoothed unigram model p_r for each register r and a background", "+     unigram model q over the whole pool.", "+  2. Score every pool doc for register r by the mean per-token log importance", "+     weight   s_r(d) = mean_{t in d} [ log p_r(t) - log q(t) ]  -- how much more", "+     \"register-r-like\" than \"generic-pool-like\" its tokens are.", "+  3. Drop web junk first: too short/long, exact duplicates, and low-diversity /", "+     whitespace-dominated boilerplate (one token > 30% of the doc, or type/token", "+     ratio < 0.30) -- these otherwise win the raw DSIR score (Apache \"Index of /\"", "+     listings, nav menus).", "+  4. Fill the 12M-token budget by round-robin across registers in the target's", "+     proportion (wiki:web+news:qa = 1:2:1), taking each register's best unused", "+     docs. This guarantees the code-heavy QA register (which the *combined*-target", "+     unigram starves, since its tokens are rare in the 75%-non-QA target) gets its", "+     fair share -- the single biggest driver of held-out perplexity here.", "+", "+Matching the training MIXTURE to the eval mixture (not memorizing the dev sample)", "+is what transfers to the hidden, disjoint official target of the same domain.", " \"\"\"", " import json, os, sys, time, numpy as np", " from transformers import AutoTokenizer"]}, {"oldStart": 28, "oldLines": 59, "newStart": 40, "newLines": 59, "lines": [" CACHE  = \"/tmp/pool_tok.npz\"", " V      = 50257", " ALPHA  = 0.5          # add-alpha smoothing for the unigram models", "-MINTOK = 50           # drop near-empty / boilerplate fragments", "-MAXTOK = 20000        # drop pathological mega-documents", "-TOP1MAX= 0.30         # drop whitespace/boilerplate: 1 token > 30% of doc", "-TTRMIN = 0.30         # drop low-diversity repetitive docs (type/token ratio)", "+MINTOK, MAXTOK = 50, 20000", "+TOP1MAX, TTRMIN = 0.30, 0.30", "+BUDGET = 12_000_000", "+# register token-index ranges in the dev target; gaps skip transition zones", "+REG    = {\"wiki\": (0, 260_000), \"web_news\": (280_000, 750_000), \"qa\": (760_000, 1_000_000)}", "+# budget split proportional to the eval mixture (web_news block = 2 registers)", "+REG_BUDGET = {\"wiki\": 3_000_000, \"web_news\": 6_000_000, \"qa\": 3_000_000}", "+PATTERN = [\"wiki\", \"web_news\", \"qa\", \"web_news\"]", " t0 = time.time()", " ", " # ---------------------------------------------------------------- tokenize pool", " tok = AutoTokenizer.from_pretrained(\"gpt2\")", " if os.path.exists(CACHE):", "-    z = np.load(CACHE)", "-    flat, offs, ids = z[\"flat\"], z[\"offs\"], z[\"ids\"]", "+    z = np.load(CACHE); flat, offs, ids = z[\"flat\"], z[\"offs\"], z[\"ids\"]", "     print(f\"[{time.time()-t0:.0f}s] loaded cache: {len(ids)} docs, {len(flat)} tokens\")", " else:", "     texts, ids = [], []", "     for line in open(POOL):", "         r = json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])", "     ids = np.array(ids, dtype=np.int64)", "-    flat_parts, lens = [], np.empty(len(texts), dtype=np.int64)", "-    B = 4000", "-    for s in range(0, len(texts), B):", "-        enc = tok(texts[s:s+B], add_special_tokens=False)[\"input_ids\"]", "-        for j, t in enumerate(enc):", "-            lens[s+j] = len(t)", "-            flat_parts.append(np.asarray(t, dtype=np.uint16))", "-    flat = np.concatenate(flat_parts)", "+    parts, lens = [], np.empty(len(texts), dtype=np.int64)", "+    for s in range(0, len(texts), 4000):", "+        for j, t in enumerate(tok(texts[s:s+4000], add_special_tokens=False)[\"input_ids\"]):", "+            lens[s+j] = len(t); parts.append(np.asarray(t, dtype=np.uint16))", "+    flat = np.concatenate(parts)", "     offs = np.zeros(len(texts)+1, dtype=np.int64); offs[1:] = np.cumsum(lens)", "     np.savez(CACHE, flat=flat, offs=offs, ids=ids)", "     print(f\"[{time.time()-t0:.0f}s] tokenized: {len(ids)} docs, {len(flat)} tokens\")", " ", "-# ---------------------------------------------------- target / pool unigram PMFs", "+flat64 = flat.astype(np.int64)", "+ntok   = offs[1:] - offs[:-1]", "+", "+# --------------------------------------------------- background + register PMFs", "+cq = np.bincount(flat64, minlength=V).astype(np.float64)", "+logpq = np.log((cq + ALPHA) / (cq.sum() + ALPHA * V))", " tgt = np.load(TARGET).astype(np.int64)", "-ct = np.bincount(tgt, minlength=V).astype(np.float64)", "-cq = np.bincount(flat.astype(np.int64), minlength=V).astype(np.float64)", "-pt = (ct + ALPHA) / (ct.sum() + ALPHA * V)", "-pq = (cq + ALPHA) / (cq.sum() + ALPHA * V)", "-w  = np.log(pt) - np.log(pq)                      # importance weight per token", "-print(f\"[{time.time()-t0:.0f}s] built unigram models; target {int(ct.sum())} tok\")", " ", "-# ------------------------------------------------------------- score every doc", "-# cumulative sum of weights so a doc's total = wc[end]-wc[start] (vectorized)", "-wflat = w[flat.astype(np.int64)]", "-wc = np.zeros(len(flat)+1, dtype=np.float64); np.cumsum(wflat, out=wc[1:])", "-ntok  = offs[1:] - offs[:-1]", "-wsum  = wc[offs[1:]] - wc[offs[:-1]]", "-score = wsum / np.maximum(ntok, 1)                # mean per-token importance", "+def mean_importance(logp):", "+    w  = logp - logpq", "+    wf = w[flat64]", "+    wc = np.zeros(len(flat) + 1); np.cumsum(wf, out=wc[1:])", "+    return (wc[offs[1:]] - wc[offs[:-1]]) / np.maximum(ntok, 1)", " ", "-# ------------------------------------------------------------------- filtering", "-# Single pass: exact-dup hash + token-diversity features (type/token ratio and", "-# most-common-token fraction). The DSIR mean score alone rewards repetitive", "-# boilerplate (e.g. Apache \"Index of /\" listings: a few whitespace tokens", "-# repeated), so we drop low-diversity / whitespace-dominated docs explicitly.", "-seen = set()", "-dup  = np.zeros(len(ids), dtype=bool)", "+score = {}", "+for r, (a, b) in REG.items():", "+    c = np.bincount(tgt[a:b], minlength=V).astype(np.float64)", "+    score[r] = mean_importance(np.log((c + ALPHA) / (c.sum() + ALPHA * V)))", "+cc = np.bincount(tgt, minlength=V).astype(np.float64)", "+score[\"comb\"] = mean_importance(np.log((cc + ALPHA) / (cc.sum() + ALPHA * V)))", "+print(f\"[{time.time()-t0:.0f}s] scored {len(REG)} registers + combined\")", "+", "+# ------------------------------------------- junk filter (dedup + diversity)", "+seen = set(); dup = np.zeros(len(ids), dtype=bool)", " ttr  = np.zeros(len(ids)); top1 = np.zeros(len(ids))", " for i in range(len(ids)):", "     s = flat[offs[i]:offs[i+1]]"]}, {"oldStart": 92, "oldLines": 37, "newStart": 104, "newLines": 31, "lines": ["         ttr[i] = len(u) / len(s); top1[i] = c.max() / len(s)", " valid = ((ntok >= MINTOK) & (ntok <= MAXTOK) & (~dup)", "          & (top1 <= TOP1MAX) & (ttr >= TTRMIN))", "-print(f\"[{time.time()-t0:.0f}s] valid docs: {valid.sum()} / {len(ids)} \"", "-      f\"(dropped {int((~valid).sum())}: dup={int(dup.sum())}, \"", "-      f\"lowdiv={int(((top1>TOP1MAX)|(ttr<TTRMIN)).sum())})\")", "+vidx = np.where(valid)[0]", "+print(f\"[{time.time()-t0:.0f}s] valid {valid.sum()}/{len(ids)} \"", "+      f\"(dup={int(dup.sum())} lowdiv={int(((top1>TOP1MAX)|(ttr<TTRMIN)).sum())})\")", " ", "-# --------------------------------------------------------- rank + write output", "-order = np.argsort(-score)                        # best-first", "-order = np.concatenate([order[valid[order]], order[~valid[order]]])  # junk last", "-sel = ids[order].tolist()", "+# ---------------------------------- balanced round-robin fill of the budget", "+order_r = {r: vidx[np.argsort(-score[r][vidx])] for r in REG}", "+ptr = {r: 0 for r in REG}; used = {r: 0 for r in REG}; taken = set(); final = []; acc = 0", "+while acc < BUDGET:", "+    prog = False", "+    for r in PATTERN:", "+        if used[r] >= REG_BUDGET[r]: continue", "+        lst = order_r[r]", "+        while ptr[r] < len(lst) and int(lst[ptr[r]]) in taken: ptr[r] += 1", "+        if ptr[r] >= len(lst): continue", "+        d = int(lst[ptr[r]]); ptr[r] += 1; taken.add(d)", "+        final.append(d); n = int(ntok[d]) + 1; used[r] += n; acc += n; prog = True", "+    if not prog: break", "+print(f\"[{time.time()-t0:.0f}s] balanced fill: {len(final)} docs {acc} tok \"", "+      f\"(wiki={used['wiki']} web_news={used['web_news']} qa={used['qa']})\")", "+", "+# ------------------ append the rest (combined score) then junk, write output", "+rest = [int(i) for i in vidx[np.argsort(-score[\"comb\"][vidx])] if int(i) not in taken]", "+junk = [int(i) for i in np.where(~valid)[0]]", "+sel  = [int(ids[i]) for i in (final + rest + junk)]", "+assert len(sel) == len(set(sel)) == len(ids)          # unique, complete", " os.makedirs(os.path.dirname(OUT), exist_ok=True)", " json.dump(sel, open(OUT, \"w\"))", "-", "-# how many docs fill the 12M budget?", "-cum = np.cumsum((ntok[order] + 1))", "-nfill = int(np.searchsorted(cum, 12_000_000)) + 1", " print(f\"[{time.time()-t0:.0f}s] wrote {len(sel)} ids -> {OUT}\")", "-print(f\"budget filled by first ~{nfill} docs ({cum[min(nfill,len(cum)-1)]} tokens)\")", "-", "-# ------------------------------------------------------------- diagnostics only", "-if \"--diag\" in sys.argv:", "-    text = {}", "-    for line in open(POOL):", "-        r = json.loads(line); text[r[\"id\"]] = r[\"text\"]", "-    print(\"\\n===== TOP 5 selected =====\")", "-    for i in order[:5]:", "-        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")", "-    print(\"\\n===== around budget cutoff (rank ~nfill) =====\")", "-    for i in order[nfill-2:nfill+1]:", "-        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")", "-    print(\"\\n===== BOTTOM 5 (valid) =====\")", "-    vo = order[valid[order]]", "-    for i in vo[-5:]:", "-        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")", "-    print(f\"\\nscore pct: p10 {np.percentile(score,10):+.3f} p50 {np.percentile(score,50):+.3f} \"", "-          f\"p90 {np.percentile(score,90):+.3f}\")"]}], "originalFile": "\"\"\"Curate a raw web pool into a priority-ordered selection for training a small LM.\n\nCriterion (stated, reproducible):\n  Select documents whose GPT-2 token distribution best matches a disclosed\n  high-quality, multi-domain English TARGET (Wikipedia + news + high-quality web\n  prose + technical Q&A), after removing obvious web junk (too short, near-empty,\n  exact duplicates).\n\n  Quality signal = DSIR-style unigram importance weight (Xie et al. 2023):\n  for each vocabulary token v,\n        w[v] = log p_target(v) - log p_pool(v)\n  (add-alpha smoothed). A document's score is the mean of w over its tokens ---\n  i.e. how much more \"target-like\" than \"generic-pool-like\" its words are.\n  Documents are emitted best-first; the training pipeline consumes them in order\n  until the 12M-token budget is filled.\n\nThe TARGET distribution is estimated from the provided dev target\n(data/multi_dev.npy), which is a sample of the disclosed HQ domain. The official\nscoring target is a *disjoint* sample of the same domain, so matching the domain\nn-gram statistics (not memorizing the dev set) is what transfers.\n\"\"\"\nimport json, os, sys, time, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL   = \"/workspace/data/pool.jsonl\"\nTARGET = \"/workspace/data/multi_dev.npy\"\nOUT    = \"/workspace/submission/selection.json\"\nCACHE  = \"/tmp/pool_tok.npz\"\nV      = 50257\nALPHA  = 0.5          # add-alpha smoothing for the unigram models\nMINTOK = 50           # drop near-empty / boilerplate fragments\nMAXTOK = 20000        # drop pathological mega-documents\nTOP1MAX= 0.30         # drop whitespace/boilerplate: 1 token > 30% of doc\nTTRMIN = 0.30         # drop low-diversity repetitive docs (type/token ratio)\nt0 = time.time()\n\n# ---------------------------------------------------------------- tokenize pool\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nif os.path.exists(CACHE):\n    z = np.load(CACHE)\n    flat, offs, ids = z[\"flat\"], z[\"offs\"], z[\"ids\"]\n    print(f\"[{time.time()-t0:.0f}s] loaded cache: {len(ids)} docs, {len(flat)} tokens\")\nelse:\n    texts, ids = [], []\n    for line in open(POOL):\n        r = json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\n    ids = np.array(ids, dtype=np.int64)\n    flat_parts, lens = [], np.empty(len(texts), dtype=np.int64)\n    B = 4000\n    for s in range(0, len(texts), B):\n        enc = tok(texts[s:s+B], add_special_tokens=False)[\"input_ids\"]\n        for j, t in enumerate(enc):\n            lens[s+j] = len(t)\n            flat_parts.append(np.asarray(t, dtype=np.uint16))\n    flat = np.concatenate(flat_parts)\n    offs = np.zeros(len(texts)+1, dtype=np.int64); offs[1:] = np.cumsum(lens)\n    np.savez(CACHE, flat=flat, offs=offs, ids=ids)\n    print(f\"[{time.time()-t0:.0f}s] tokenized: {len(ids)} docs, {len(flat)} tokens\")\n\n# ---------------------------------------------------- target / pool unigram PMFs\ntgt = np.load(TARGET).astype(np.int64)\nct = np.bincount(tgt, minlength=V).astype(np.float64)\ncq = np.bincount(flat.astype(np.int64), minlength=V).astype(np.float64)\npt = (ct + ALPHA) / (ct.sum() + ALPHA * V)\npq = (cq + ALPHA) / (cq.sum() + ALPHA * V)\nw  = np.log(pt) - np.log(pq)                      # importance weight per token\nprint(f\"[{time.time()-t0:.0f}s] built unigram models; target {int(ct.sum())} tok\")\n\n# ------------------------------------------------------------- score every doc\n# cumulative sum of weights so a doc's total = wc[end]-wc[start] (vectorized)\nwflat = w[flat.astype(np.int64)]\nwc = np.zeros(len(flat)+1, dtype=np.float64); np.cumsum(wflat, out=wc[1:])\nntok  = offs[1:] - offs[:-1]\nwsum  = wc[offs[1:]] - wc[offs[:-1]]\nscore = wsum / np.maximum(ntok, 1)                # mean per-token importance\n\n# ------------------------------------------------------------------- filtering\n# Single pass: exact-dup hash + token-diversity features (type/token ratio and\n# most-common-token fraction). The DSIR mean score alone rewards repetitive\n# boilerplate (e.g. Apache \"Index of /\" listings: a few whitespace tokens\n# repeated), so we drop low-diversity / whitespace-dominated docs explicitly.\nseen = set()\ndup  = np.zeros(len(ids), dtype=bool)\nttr  = np.zeros(len(ids)); top1 = np.zeros(len(ids))\nfor i in range(len(ids)):\n    s = flat[offs[i]:offs[i+1]]\n    h = hash(s.tobytes())\n    if h in seen: dup[i] = True\n    else: seen.add(h)\n    if len(s):\n        u, c = np.unique(s, return_counts=True)\n        ttr[i] = len(u) / len(s); top1[i] = c.max() / len(s)\nvalid = ((ntok >= MINTOK) & (ntok <= MAXTOK) & (~dup)\n         & (top1 <= TOP1MAX) & (ttr >= TTRMIN))\nprint(f\"[{time.time()-t0:.0f}s] valid docs: {valid.sum()} / {len(ids)} \"\n      f\"(dropped {int((~valid).sum())}: dup={int(dup.sum())}, \"\n      f\"lowdiv={int(((top1>TOP1MAX)|(ttr<TTRMIN)).sum())})\")\n\n# --------------------------------------------------------- rank + write output\norder = np.argsort(-score)                        # best-first\norder = np.concatenate([order[valid[order]], order[~valid[order]]])  # junk last\nsel = ids[order].tolist()\nos.makedirs(os.path.dirname(OUT), exist_ok=True)\njson.dump(sel, open(OUT, \"w\"))\n\n# how many docs fill the 12M budget?\ncum = np.cumsum((ntok[order] + 1))\nnfill = int(np.searchsorted(cum, 12_000_000)) + 1\nprint(f\"[{time.time()-t0:.0f}s] wrote {len(sel)} ids -> {OUT}\")\nprint(f\"budget filled by first ~{nfill} docs ({cum[min(nfill,len(cum)-1)]} tokens)\")\n\n# ------------------------------------------------------------- diagnostics only\nif \"--diag\" in sys.argv:\n    text = {}\n    for line in open(POOL):\n        r = json.loads(line); text[r[\"id\"]] = r[\"text\"]\n    print(\"\\n===== TOP 5 selected =====\")\n    for i in order[:5]:\n        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")\n    print(\"\\n===== around budget cutoff (rank ~nfill) =====\")\n    for i in order[nfill-2:nfill+1]:\n        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")\n    print(\"\\n===== BOTTOM 5 (valid) =====\")\n    vo = order[valid[order]]\n    for i in vo[-5:]:\n        print(f\"[score {score[i]:+.3f} ntok {ntok[i]}] {text[ids[i]][:220]!r}\")\n    print(f\"\\nscore pct: p10 {np.percentile(score,10):+.3f} p50 {np.percentile(score,50):+.3f} \"\n          f\"p90 {np.percentile(score,90):+.3f}\")\n", "userModified": false}

[1s] loaded cache: 182016 docs, 199911772 tokens
[9s] scored 3 registers + combined
[14s] valid 156760/182016 (dup=11753 lowdiv=13899)
[15s] balanced fill: 19938 docs 12000382 tok (wiki=3000020 web_news=6001987 qa=2998375)
[15s] wrote 182016 ids -> /workspace/submission/selection.json
=== verify selection.json matches /tmp/sel_bal.json ===
len 182016 unique 182016 in-range 0 182015
prefix(20000) identical to sel_bal: True
fully identical: True
===timer===
3968

[stdout]
[1s] loaded cache: 182016 docs, 199911772 tokens
[9s] scored 3 registers + combined
[14s] valid 156760/182016 (dup=11753 lowdiv=13899)
[15s] balanced fill: 19938 docs 12000382 tok (wiki=3000020 web_news=6001987 qa=2998375)
[15s] wrote 182016 ids -> /workspace/submission/selection.json
=== verify selection.json matches /tmp/sel_bal.json ===
len 182016 unique 182016 in-range 0 182015
prefix(20000) identical to sel_bal: True
fully identical: True
===timer===
3968
[metadata] {"noOutputExpected": false}

File created successfully at: /tmp/rank4.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/rank4.py", "content": "import json, time, numpy as np\nt0=time.time()\nz=np.load(\"/tmp/pool_tok.npz\"); flat,offs,ids=z[\"flat\"],z[\"offs\"],z[\"ids\"]\nf=np.load(\"/tmp/feats.npz\"); ttr,top1=f[\"ttr\"],f[\"top1\"]\nV=50257; ALPHA=0.5; flat64=flat.astype(np.int64); ntok=offs[1:]-offs[:-1]\ntgt=np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64)\ncq=np.bincount(flat64,minlength=V).astype(np.float64); logpq=np.log((cq+ALPHA)/(cq.sum()+ALPHA*V))\ndef mi(logp):\n    w=logp-logpq; wf=w[flat64]; wc=np.zeros(len(flat)+1); np.cumsum(wf,out=wc[1:])\n    return (wc[offs[1:]]-wc[offs[:-1]])/np.maximum(ntok,1)\n# 4-way split: web prose vs news inside the middle block\nREG={\"wiki\":(0,260000),\"web\":(280000,515000),\"news\":(515000,750000),\"qa\":(760000,1000000)}\nscore={r:mi(np.log((np.bincount(tgt[a:b],minlength=V).astype(np.float64)+ALPHA)/((b-a)+ALPHA*V))) for r,(a,b) in REG.items()}\ncc=np.bincount(tgt,minlength=V).astype(np.float64); score[\"comb\"]=mi(np.log((cc+ALPHA)/(cc.sum()+ALPHA*V)))\nseen=set(); dup=np.zeros(len(ids),dtype=bool)\nfor i in range(len(ids)):\n    h=hash(flat[offs[i]:offs[i+1]].tobytes())\n    if h in seen: dup[i]=True\n    else: seen.add(h)\nvalid=(ntok>=50)&(ntok<=20000)&(top1<=0.30)&(ttr>=0.30)&(~dup); vidx=np.where(valid)[0]\norder_r={r:vidx[np.argsort(-score[r][vidx])] for r in REG}\nREGB={r:3_000_000 for r in REG}; PATTERN=[\"wiki\",\"web\",\"news\",\"qa\"]\nptr={r:0 for r in REG}; used={r:0 for r in REG}; taken=set(); final=[]; acc=0\nwhile acc<12_000_000:\n    prog=False\n    for r in PATTERN:\n        if used[r]>=REGB[r]: continue\n        lst=order_r[r]\n        while ptr[r]<len(lst) and int(lst[ptr[r]]) in taken: ptr[r]+=1\n        if ptr[r]>=len(lst): continue\n        d=int(lst[ptr[r]]); ptr[r]+=1; taken.add(d); final.append(d)\n        n=int(ntok[d])+1; used[r]+=n; acc+=n; prog=True\n    if not prog: break\nprint(f\"[{time.time()-t0:.0f}s] 4way fill {len(final)} docs {acc} tok\",{r:used[r] for r in REG})\nrest=[int(i) for i in vidx[np.argsort(-score[\"comb\"][vidx])] if int(i) not in taken]\njunk=[int(i) for i in np.where(~valid)[0]]\nsel=[int(ids[i]) for i in (final+rest+junk)]\njson.dump(sel,open(\"/tmp/sel_4way.json\",\"w\")); print(\"wrote /tmp/sel_4way.json\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bol2ik2aq). Output is being written to: /tmp/claude-0/-workspace/a3287dea-93cc-416d-bede-510e37b6263d/tasks/bol2ik2aq.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": "bol2ik2aq", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 323.978  loss 5.7807  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3767

[stdout]
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 323.978  loss 5.7807  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3767
[metadata] {"noOutputExpected": false}

fill {'wiki': 3601065, 'web_news': 4800907, 'qa': 3598063}
wrote 182016
eval pid 2659

[stdout]
fill {'wiki': 3601065, 'web_news': 4800907, 'qa': 3598063}
wrote 182016
eval pid 2659
[metadata] {"noOutputExpected": false}

combined-DSIR: code/QA-like docs in first 18000 = 151 (0.8%)
balanced   : code/QA-like docs in first 18000 = 440 (2.4%)
===timer===
3697

[stdout]
combined-DSIR: code/QA-like docs in first 18000 = 151 (0.8%)
balanced   : code/QA-like docs in first 18000 = 440 (2.4%)
===timer===
3697
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
===timer===
3637

[stdout]
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
===timer===
3637
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 321.160  loss 5.7719  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3607

[stdout]
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 321.160  loss 5.7719  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3607
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: b4utwsdp7). Output is being written to: /tmp/claude-0/-workspace/a3287dea-93cc-416d-bede-510e37b6263d/tasks/b4utwsdp7.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": "b4utwsdp7", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 318.070  loss 5.7623  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3427

[stdout]
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 318.070  loss 5.7623  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3427
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/tmp/mkbal.py", "content": "import json, sys, numpy as np\n# args: wiki_M web_news_M qa_M outfile\nbw,bm,bq = float(sys.argv[1]),float(sys.argv[2]),float(sys.argv[3]); OUT=sys.argv[4]\nz=np.load(\"/tmp/pool_tok.npz\"); flat,offs,ids=z[\"flat\"],z[\"offs\"],z[\"ids\"]\nf=np.load(\"/tmp/feats.npz\"); ttr,top1=f[\"ttr\"],f[\"top1\"]\nV=50257; ALPHA=0.5; flat64=flat.astype(np.int64); ntok=offs[1:]-offs[:-1]\ntgt=np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64)\ncq=np.bincount(flat64,minlength=V).astype(np.float64); logpq=np.log((cq+ALPHA)/(cq.sum()+ALPHA*V))\ndef mi(lp):\n    w=lp-logpq; wf=w[flat64]; wc=np.zeros(len(flat)+1); np.cumsum(wf,out=wc[1:]); return (wc[offs[1:]]-wc[offs[:-1]])/np.maximum(ntok,1)\nREG={\"wiki\":(0,260000),\"web_news\":(280000,750000),\"qa\":(760000,1000000)}\nscore={r:mi(np.log((np.bincount(tgt[a:b],minlength=V).astype(np.float64)+ALPHA)/((b-a)+ALPHA*V))) for r,(a,b) in REG.items()}\ncc=np.bincount(tgt,minlength=V).astype(np.float64); score[\"comb\"]=mi(np.log((cc+ALPHA)/(cc.sum()+ALPHA*V)))\nseen=set(); dup=np.zeros(len(ids),dtype=bool)\nfor i in range(len(ids)):\n    h=hash(flat[offs[i]:offs[i+1]].tobytes()); dup[i]= h in seen; seen.add(h)\nvalid=(ntok>=50)&(ntok<=20000)&(top1<=0.30)&(ttr>=0.30)&(~dup); vidx=np.where(valid)[0]\norder_r={r:vidx[np.argsort(-score[r][vidx])] for r in REG}\nREGB={\"wiki\":bw*1e6,\"web_news\":bm*1e6,\"qa\":bq*1e6}\n# round-robin weighted by budget ratio so all registers fill together\nPAT=[\"wiki\",\"qa\",\"web_news\",\"wiki\",\"qa\"] if bm<4.5 else [\"wiki\",\"web_news\",\"qa\",\"web_news\"]\nptr={r:0 for r in REG}; used={r:0 for r in REG}; taken=set(); final=[]; acc=0\nwhile acc<12_000_000:\n    prog=False\n    for r in PAT:\n        if used[r]>=REGB[r]: continue\n        lst=order_r[r]\n        while ptr[r]<len(lst) and int(lst[ptr[r]]) in taken: ptr[r]+=1\n        if ptr[r]>=len(lst): continue\n        d=int(lst[ptr[r]]); ptr[r]+=1; taken.add(d); final.append(d); used[r]+=int(ntok[d])+1; acc+=int(ntok[d])+1; prog=True\n    if not prog: break\nprint(\"fill\",{r:int(used[r]) for r in REG})\nrest=[int(i) for i in vidx[np.argsort(-score[\"comb\"][vidx])] if int(i) not in taken]\nsel=[int(ids[i]) for i in (final+rest+[int(i) for i in np.where(~valid)[0]])]\njson.dump(sel,open(OUT,\"w\")); print(\"wrote\",OUT,len(sel))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bi6khjr5h). Output is being written to: /tmp/claude-0/-workspace/a3287dea-93cc-416d-bede-510e37b6263d/tasks/bi6khjr5h.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": "bi6khjr5h", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 321.094  loss 5.7717  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3247

[stdout]
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 321.094  loss 5.7717  train_s 55  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3247
[metadata] {"noOutputExpected": false}

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": "# budget split proportional to the eval mixture (web_news block = 2 registers)\nREG_BUDGET = {\"wiki\": 3_000_000, \"web_news\": 6_000_000, \"qa\": 3_000_000}\nPATTERN = [\"wiki\", \"web_news\", \"qa\", \"web_news\"]", "newString": "# Budget split across registers. A proportional split (3M/6M/3M = matching the\n# eval token mixture) already scores well, but the eval PPL is a token-weighted\n# mean of per-register loss, and the middle (web+news) register is the lowest-loss\n# / most-redundant one: shifting budget from it to the higher-loss wiki and QA\n# registers lowers the weighted mean. Empirically (dev PPL) the loss is a smooth\n# valley minimized near 35/30/35, not at the proportional 25/50/25:\n#   25/50/25 -> 323.4 | 30/40/30 -> 321.2 | 35/30/35 -> 318.1 | 40/20/40 -> 321.1\nREG_BUDGET = {\"wiki\": 4_200_000, \"web_news\": 3_600_000, \"qa\": 4_200_000}\nPATTERN = [\"wiki\", \"qa\", \"web_news\", \"wiki\", \"qa\"]", "originalFile": "\"\"\"Curate a raw web pool into a priority-ordered selection for training a small LM.\n\nSTATED CRITERION (reproducible; no hand-picked ids)\n---------------------------------------------------\nThe scoring target is a BROAD, multi-domain high-quality English corpus made of\nfour equal registers: encyclopedic (Wikipedia), high-quality web prose, news, and\ntechnical Q&A. Inspecting the provided dev target (data/multi_dev.npy) shows it is\nlaid out in three contiguous register blocks:\n    Wikipedia   tokens      0 .. 260k   (~25%)\n    web + news  tokens   280k .. 750k   (~50%)\n    technical QA tokens  760k .. 1000k  (~25%)\n\nWe select documents by DSIR-style n-gram importance weighting (Xie et al. 2023),\napplied *per register* and then balanced to the target's register proportions:\n\n  1. Estimate a smoothed unigram model p_r for each register r and a background\n     unigram model q over the whole pool.\n  2. Score every pool doc for register r by the mean per-token log importance\n     weight   s_r(d) = mean_{t in d} [ log p_r(t) - log q(t) ]  -- how much more\n     \"register-r-like\" than \"generic-pool-like\" its tokens are.\n  3. Drop web junk first: too short/long, exact duplicates, and low-diversity /\n     whitespace-dominated boilerplate (one token > 30% of the doc, or type/token\n     ratio < 0.30) -- these otherwise win the raw DSIR score (Apache \"Index of /\"\n     listings, nav menus).\n  4. Fill the 12M-token budget by round-robin across registers in the target's\n     proportion (wiki:web+news:qa = 1:2:1), taking each register's best unused\n     docs. This guarantees the code-heavy QA register (which the *combined*-target\n     unigram starves, since its tokens are rare in the 75%-non-QA target) gets its\n     fair share -- the single biggest driver of held-out perplexity here.\n\nMatching the training MIXTURE to the eval mixture (not memorizing the dev sample)\nis what transfers to the hidden, disjoint official target of the same domain.\n\"\"\"\nimport json, os, sys, time, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL   = \"/workspace/data/pool.jsonl\"\nTARGET = \"/workspace/data/multi_dev.npy\"\nOUT    = \"/workspace/submission/selection.json\"\nCACHE  = \"/tmp/pool_tok.npz\"\nV      = 50257\nALPHA  = 0.5          # add-alpha smoothing for the unigram models\nMINTOK, MAXTOK = 50, 20000\nTOP1MAX, TTRMIN = 0.30, 0.30\nBUDGET = 12_000_000\n# register token-index ranges in the dev target; gaps skip transition zones\nREG    = {\"wiki\": (0, 260_000), \"web_news\": (280_000, 750_000), \"qa\": (760_000, 1_000_000)}\n# budget split proportional to the eval mixture (web_news block = 2 registers)\nREG_BUDGET = {\"wiki\": 3_000_000, \"web_news\": 6_000_000, \"qa\": 3_000_000}\nPATTERN = [\"wiki\", \"web_news\", \"qa\", \"web_news\"]\nt0 = time.time()\n\n# ---------------------------------------------------------------- tokenize pool\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nif os.path.exists(CACHE):\n    z = np.load(CACHE); flat, offs, ids = z[\"flat\"], z[\"offs\"], z[\"ids\"]\n    print(f\"[{time.time()-t0:.0f}s] loaded cache: {len(ids)} docs, {len(flat)} tokens\")\nelse:\n    texts, ids = [], []\n    for line in open(POOL):\n        r = json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\n    ids = np.array(ids, dtype=np.int64)\n    parts, lens = [], np.empty(len(texts), dtype=np.int64)\n    for s in range(0, len(texts), 4000):\n        for j, t in enumerate(tok(texts[s:s+4000], add_special_tokens=False)[\"input_ids\"]):\n            lens[s+j] = len(t); parts.append(np.asarray(t, dtype=np.uint16))\n    flat = np.concatenate(parts)\n    offs = np.zeros(len(texts)+1, dtype=np.int64); offs[1:] = np.cumsum(lens)\n    np.savez(CACHE, flat=flat, offs=offs, ids=ids)\n    print(f\"[{time.time()-t0:.0f}s] tokenized: {len(ids)} docs, {len(flat)} tokens\")\n\nflat64 = flat.astype(np.int64)\nntok   = offs[1:] - offs[:-1]\n\n# --------------------------------------------------- background + register PMFs\ncq = np.bincount(flat64, minlength=V).astype(np.float64)\nlogpq = np.log((cq + ALPHA) / (cq.sum() + ALPHA * V))\ntgt = np.load(TARGET).astype(np.int64)\n\ndef mean_importance(logp):\n    w  = logp - logpq\n    wf = w[flat64]\n    wc = np.zeros(len(flat) + 1); np.cumsum(wf, out=wc[1:])\n    return (wc[offs[1:]] - wc[offs[:-1]]) / np.maximum(ntok, 1)\n\nscore = {}\nfor r, (a, b) in REG.items():\n    c = np.bincount(tgt[a:b], minlength=V).astype(np.float64)\n    score[r] = mean_importance(np.log((c + ALPHA) / (c.sum() + ALPHA * V)))\ncc = np.bincount(tgt, minlength=V).astype(np.float64)\nscore[\"comb\"] = mean_importance(np.log((cc + ALPHA) / (cc.sum() + ALPHA * V)))\nprint(f\"[{time.time()-t0:.0f}s] scored {len(REG)} registers + combined\")\n\n# ------------------------------------------- junk filter (dedup + diversity)\nseen = set(); dup = np.zeros(len(ids), dtype=bool)\nttr  = np.zeros(len(ids)); top1 = np.zeros(len(ids))\nfor i in range(len(ids)):\n    s = flat[offs[i]:offs[i+1]]\n    h = hash(s.tobytes())\n    if h in seen: dup[i] = True\n    else: seen.add(h)\n    if len(s):\n        u, c = np.unique(s, return_counts=True)\n        ttr[i] = len(u) / len(s); top1[i] = c.max() / len(s)\nvalid = ((ntok >= MINTOK) & (ntok <= MAXTOK) & (~dup)\n         & (top1 <= TOP1MAX) & (ttr >= TTRMIN))\nvidx = np.where(valid)[0]\nprint(f\"[{time.time()-t0:.0f}s] valid {valid.sum()}/{len(ids)} \"\n      f\"(dup={int(dup.sum())} lowdiv={int(((top1>TOP1MAX)|(ttr<TTRMIN)).sum())})\")\n\n# ---------------------------------- balanced round-robin fill of the budget\norder_r = {r: vidx[np.argsort(-score[r][vidx])] for r in REG}\nptr = {r: 0 for r in REG}; used = {r: 0 for r in REG}; taken = set(); final = []; acc = 0\nwhile acc < BUDGET:\n    prog = False\n    for r in PATTERN:\n        if used[r] >= REG_BUDGET[r]: continue\n        lst = order_r[r]\n        while ptr[r] < len(lst) and int(lst[ptr[r]]) in taken: ptr[r] += 1\n        if ptr[r] >= len(lst): continue\n        d = int(lst[ptr[r]]); ptr[r] += 1; taken.add(d)\n        final.append(d); n = int(ntok[d]) + 1; used[r] += n; acc += n; prog = True\n    if not prog: break\nprint(f\"[{time.time()-t0:.0f}s] balanced fill: {len(final)} docs {acc} tok \"\n      f\"(wiki={used['wiki']} web_news={used['web_news']} qa={used['qa']})\")\n\n# ------------------ append the rest (combined score) then junk, write output\nrest = [int(i) for i in vidx[np.argsort(-score[\"comb\"][vidx])] if int(i) not in taken]\njunk = [int(i) for i in np.where(~valid)[0]]\nsel  = [int(ids[i]) for i in (final + rest + junk)]\nassert len(sel) == len(set(sel)) == len(ids)          # unique, complete\nos.makedirs(os.path.dirname(OUT), exist_ok=True)\njson.dump(sel, open(OUT, \"w\"))\nprint(f\"[{time.time()-t0:.0f}s] wrote {len(sel)} ids -> {OUT}\")\n", "structuredPatch": [{"oldStart": 45, "oldLines": 9, "newStart": 45, "newLines": 15, "lines": [" BUDGET = 12_000_000", " # register token-index ranges in the dev target; gaps skip transition zones", " REG    = {\"wiki\": (0, 260_000), \"web_news\": (280_000, 750_000), \"qa\": (760_000, 1_000_000)}", "-# budget split proportional to the eval mixture (web_news block = 2 registers)", "-REG_BUDGET = {\"wiki\": 3_000_000, \"web_news\": 6_000_000, \"qa\": 3_000_000}", "-PATTERN = [\"wiki\", \"web_news\", \"qa\", \"web_news\"]", "+# Budget split across registers. A proportional split (3M/6M/3M = matching the", "+# eval token mixture) already scores well, but the eval PPL is a token-weighted", "+# mean of per-register loss, and the middle (web+news) register is the lowest-loss", "+# / most-redundant one: shifting budget from it to the higher-loss wiki and QA", "+# registers lowers the weighted mean. Empirically (dev PPL) the loss is a smooth", "+# valley minimized near 35/30/35, not at the proportional 25/50/25:", "+#   25/50/25 -> 323.4 | 30/40/30 -> 321.2 | 35/30/35 -> 318.1 | 40/20/40 -> 321.1", "+REG_BUDGET = {\"wiki\": 4_200_000, \"web_news\": 3_600_000, \"qa\": 4_200_000}", "+PATTERN = [\"wiki\", \"qa\", \"web_news\", \"wiki\", \"qa\"]", " t0 = time.time()", " ", " # ---------------------------------------------------------------- tokenize pool"]}], "userModified": false, "replaceAll": false}

[1s] loaded cache: 182016 docs, 199911772 tokens
[9s] scored 3 registers + combined
[14s] valid 156760/182016 (dup=11753 lowdiv=13899)
[14s] balanced fill: 20751 docs 12000167 tok (wiki=4201993 web_news=3597876 qa=4200298)
[15s] wrote 182016 ids -> /workspace/submission/selection.json
=== verify reproduces sel_35_30_35 ===
len 182016 unique 182016 range 0 182015 | identical to 35/30/35: True
===timer===
3187

[stdout]
[1s] loaded cache: 182016 docs, 199911772 tokens
[9s] scored 3 registers + combined
[14s] valid 156760/182016 (dup=11753 lowdiv=13899)
[14s] balanced fill: 20751 docs 12000167 tok (wiki=4201993 web_news=3597876 qa=4200298)
[15s] wrote 182016 ids -> /workspace/submission/selection.json
=== verify reproduces sel_35_30_35 ===
len 182016 unique 182016 range 0 182015 | identical to 35/30/35: True
===timer===
3187
[metadata] {"noOutputExpected": false}

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-matched, capacity-aware data selection beats similarity ranking\n\n## Result summary (frozen 30M GPT, 12M-token budget, dev = `multi_dev.npy`)\n\n| Selection (12M tokens, priority-packed)                         | dev PPL |\n|-----------------------------------------------------------------|--------:|\n| Random pool sample (do-nothing baseline)                        | 457.99  |\n| DSIR similarity to the **combined** target + junk filter        | 408.46  |\n| Per-register DSIR, **balanced** 25/50/25 (proportional)         | 323.41  |\n| Per-register DSIR, **capacity-tuned** 35/30/35 (**submitted**)  | **318.07** |\n\nThe submitted selection is ~30% lower perplexity than the random baseline.\n\n## Hypothesis\nUnder a fixed, tiny token budget, held-out perplexity on a **broad multi-domain**\ntarget is governed by the training set's **register mixture**, not by aggregate\ndocument→target similarity. Two concrete sub-claims:\n\n1. **Coverage.** Selecting documents by *per-register* n-gram importance with\n   balanced quotas beats ranking every document by similarity to the *combined*\n   target, because a single combined-target unigram model **starves the\n   high-entropy minority register** (technical Q&A / code): code tokens are rare\n   in a target that is 75% non-code, so code-bearing docs never rank near the top.\n2. **Capacity allocation.** Because eval PPL is a token-weighted mean of\n   per-register loss, the optimal training mixture **over-weights the higher-loss\n   registers** (encyclopedic + Q&A) relative to their eval token share, and\n   **under-weights the low-loss, redundant middle** (web prose + news).\n\n## Mechanism → observable predictions (not the final PPL)\n\n- **P1 — composition (CONFIRMED).** The combined-similarity selection should\n  contain far fewer code/Q&A documents than the target's ~25% Q&A share; balanced\n  selection restores it. *Measured* fraction of code/Q&A-heavy docs in the 12M\n  budget region: **0.8% (combined) → 2.4% (balanced), a 3× increase.** The\n  combined ranker demonstrably starves the register.\n\n- **P2 — mixture response (CONFIRMED).** Sweeping budget from the middle register\n  toward wiki+Q&A should trace a **smooth convex valley with a single interior\n  minimum** (not monotone), because starving the 50%-mass middle eventually\n  dominates. *Measured:* 25/50/25 → **323.4**, 30/40/30 → **321.2**, 35/30/35 →\n  **318.1**, 40/20/40 → **321.1** — a convex valley minimized near 35/30/35.\n\n- **P3 — per-register loss (falsifiable, not run — no checkpoint is saved).**\n  Going combined → balanced, the held-out-loss drop should be **concentrated in\n  the Q&A and Wikipedia windows** of the target, with the news/web windows roughly\n  unchanged or slightly worse. If the gain instead came from the news window, the\n  coverage mechanism would be wrong.\n\n## Falsification\nThe claim is false if any of:\n- The combined-similarity selection already contains ~25% code/Q&A docs (no\n  starvation) — refuted by P1 (0.8%).\n- The mixture sweep is flat or monotone with no interior optimum (no\n  capacity-allocation effect) — refuted by P2 (convex valley).\n- A register-*agnostic* quality ranking at the same junk filter reaches ~318 —\n  refuted: combined-DSIR is exactly that and scores 408 ≫ 318.\n- Per-register loss shows the balanced gain is driven by the news/web register,\n  not Q&A/Wikipedia (P3).\n\n## Transfer\n- The principle — **select per domain by importance weighting, then allocate the\n  token budget toward the higher-loss domains** — transfers to any fixed-budget\n  pretraining with a known multi-domain eval: multilingual mixtures, code+prose,\n  topic mixtures. It predicts the balanced advantage **grows** whenever the target\n  contains a high-entropy minority register (code, math, a low-resource language)\n  that similarity ranking neglects.\n- The optimal over-weighting of hard registers should **shrink as budget or model\n  capacity grows** (capacity pressure eases) and as registers approach equal\n  entropy — a testable dependency.\n- It should hold on the **hidden official target**: it is the same domain with the\n  same \"equal-parts\" register proportions, and the mixture optimum depends on\n  register entropy and the fixed model capacity, not on the specific dev sample —\n  so matching the mixture (not memorizing dev n-grams) is what carries over.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Updated task #1 status

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

Updated task #3 status

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

final eval pid 3581
[1]+  Done                    nohup bash dev_eval.sh /workspace/submission/selection.json > /tmp/eval_final.log 2>&1
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 318.070  loss 5.7623  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3006

[stdout]
final eval pid 3581
[1]+  Done                    nohup bash dev_eval.sh /workspace/submission/selection.json > /tmp/eval_final.log 2>&1
packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy
HELDOUT_PPL 318.070  loss 5.7623  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
===timer===
3006
[metadata] {"noOutputExpected": false}

=== deliverables ===
-rw-r--r-- 1 root root    4372 Aug  4 05:02 /workspace/claim.md
-rw-r--r-- 1 root root    7082 Aug  4 05:04 /workspace/submission/curate.py
-rw-r--r-- 1 root root 1345018 Aug  4 05:01 /workspace/submission/selection.json
=== selection.json validity ===
type list: True
n ids: 182016 | unique: 182016 | min/max: 0 182015
all ints: True
=== curate.py in submission reproduces same selection? ===
selection.json md5: 1a8758d66bc4a7eed8c402a231c233b5
===timer===
2986

[stdout]
=== deliverables ===
-rw-r--r-- 1 root root    4372 Aug  4 05:02 /workspace/claim.md
-rw-r--r-- 1 root root    7082 Aug  4 05:04 /workspace/submission/curate.py
-rw-r--r-- 1 root root 1345018 Aug  4 05:01 /workspace/submission/selection.json
=== selection.json validity ===
type list: True
n ids: 182016 | unique: 182016 | min/max: 0 182015
all ints: True
=== curate.py in submission reproduces same selection? ===
selection.json md5: 1a8758d66bc4a7eed8c402a231c233b5
===timer===
2986
[metadata] {"noOutputExpected": false}

Updated task #2 status

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