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RSI Bench
nano-gpt-data-curation

claude-opus-4-8

nano-gpt-data-curation — graded by the task's verifier against a measured baseline.

backend
modal
variant
claude claude-opus-4-8
metric
held-out multi-domain perplexity
actions
66
duration
46m
recorded
2026-08-04

run iddclm-nano-curation__BJAS84o

Everything claude-opus-4-8 did on this task, in order.

download JSON ↗
claude-codeclaude-opus-4-866 turns40 narration66 action66 observation26 reasoning1 final
#26
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashRun bigram-backoff exploration (CPU) while eval trains on GPU
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false; python3 /tmp/explore2.py 2>&1 | grep -v Warning | tail -12",
  "description": "Run bigram-backoff exploration (CPU) while eval trains on GPU",
  "timeout": 300000
}
Bash result
######## bigram_backoff_targetLM ########
[-4.863 id174026]  Leo Ruickbie | All Rights Reserved<|endoftext|>Championship of USSR(Young Masters)- Batoumi 1969 Rk Name 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P 1 G
[-5.144 id32526] My dad has a green card and went back home over 2 years ago. He is now trying to come back after being away from the US for 2 years. I was told by som
[-5.506 id26976] <|endoftext|>Made this last night so I thought I'd post it! Using the Records sheets on the 'In a Row' CD. Let me know if you'd like it as a project! 
[-5.570 id149749]  New 2005 - Hottest Photo Ablum Unleashed - Exclusive! StarOriental - The Most Deluxe Image Database On The Net UNLEASHED - FIRST PUBLISH - 500,000 IM
[-5.574 id6656] Well, I made myself a blog I’ve been thinking of doing this for a while, largely as a journalling exercise for myself. I’m not writing for an audience
[-5.602 id127093]  Band Network, John Fitzgerald<|endoftext|>What's New 2005 - Hottest Photo Ablum Unleashed - Exclusive! StarOriental - The Most Deluxe Image Database 
[-5.666 id69097]  you have come to this section of the website because you need to arrange a funeral then this is likely to be a difficult time for you and so we hope 
[-5.706 id97206] icator.<|endoftext|>The problem is, in hardware you can’t build a computer that’s twice as good as anyone else’s anymore. Too many people know how to

[stdout]
######## bigram_backoff_targetLM ########
[-4.863 id174026]  Leo Ruickbie | All Rights Reserved<|endoftext|>Championship of USSR(Young Masters)- Batoumi 1969 Rk Name 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P 1 G
[-5.144 id32526] My dad has a green card and went back home over 2 years ago. He is now trying to come back after being away from the US for 2 years. I was told by som
[-5.506 id26976] <|endoftext|>Made this last night so I thought I'd post it! Using the Records sheets on the 'In a Row' CD. Let me know if you'd like it as a project! 
[-5.570 id149749]  New 2005 - Hottest Photo Ablum Unleashed - Exclusive! StarOriental - The Most Deluxe Image Database On The Net UNLEASHED - FIRST PUBLISH - 500,000 IM
[-5.574 id6656] Well, I made myself a blog I’ve been thinking of doing this for a while, largely as a journalling exercise for myself. I’m not writing for an audience
[-5.602 id127093]  Band Network, John Fitzgerald<|endoftext|>What's New 2005 - Hottest Photo Ablum Unleashed - Exclusive! StarOriental - The Most Deluxe Image Database 
[-5.666 id69097]  you have come to this section of the website because you need to arrange a funeral then this is likely to be a difficult time for you and so we hope 
[-5.706 id97206] icator.<|endoftext|>The problem is, in hardware you can’t build a computer that’s twice as good as anyone else’s anymore. Too many people know how to
[metadata] {"noOutputExpected": false}
#27
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolRead
args
{
  "file_path": "/tmp/tlm_eval.log"
}
Read result
1	Token indices sequence length is longer than the specified maximum sequence length for this model (1293 > 1024). Running this sequence through the model will result in indexing errors
2	packed 12000000 tokens from 20275 ids -> /tmp/dev_train.npy
3	HELDOUT_PPL 610.431  loss 6.4142  train_s 55  /tmp/dev_train.npy
4	(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
5	DONE
6

[metadata] {"type": "text", "file": {"filePath": "/tmp/tlm_eval.log", "content": "Token indices sequence length is longer than the specified maximum sequence length for this model (1293 > 1024). Running this sequence through the model will result in indexing errors\npacked 12000000 tokens from 20275 ids -> /tmp/dev_train.npy\nHELDOUT_PPL 610.431  loss 6.4142  train_s 55  /tmp/dev_train.npy\n(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)\nDONE\n", "numLines": 6, "startLine": 1, "totalLines": 6}}
#28
narration

Surprising and important: fluency-max gave PPL 610 — worse than random (458). Taking the top most-target-like docs collapses diversity: the model overfits a narrow slice (generic blogs/simple prose) and generalizes worse to the broad multi-register target. The lesson: for a broad target I need junk removal + preserved diversity, not extreme-quality ranking. Let me examine the score distribution to design a filter-not-rank approach.

toolBashAnalyze unigram score percentiles and sample docs
args
{
  "command": "cd /workspace; python3 -c \"\nimport json,numpy as np\nsu=np.load('/tmp/s_uni2.npy'); ids=np.load('/tmp/ids.npy')\nvalid=su>-8e8\nprint('valid docs',valid.sum(),'of',len(su))\nv=su[valid]\nfor p in [1,5,10,25,50,75,90,99]:\n    print(f'p{p:>2}: {np.percentile(v,p):.3f}')\ntext={}\nfor line in open('data/pool.jsonl'):\n    r=json.loads(line); text[r['id']]=r['text']\n# show docs near various percentiles\nimport numpy as np\norder=np.argsort(su)  # ascending\nvi=[k for k in order if su[k]>-8e8]\nn=len(vi)\nfor name,frac in [('p2',0.02),('p15',0.15),('p40',0.40),('p65',0.65),('p85',0.85)]:\n    k=vi[int(n*frac)]\n    print(f'--- {name} score={su[k]:.3f} id{ids[k]} ---')\n    print(text[int(ids[k])][:180].replace(chr(10),' '))\n\"",
  "description": "Analyze unigram score percentiles and sample docs"
}
Bash result
valid docs 180129 of 182016
p 1: -11.044
p 5: -10.156
p10: -9.677
p25: -8.886
p50: -8.166
p75: -7.720
p90: -7.443
p99: -7.077
--- p2 score=-10.676 id119572 ---
 uk<|endoftext|>free chat - Shemalewebcams | Free Live Sex Chat For an ideal user experience please enable JavaScript in your web browser Settings! Live Cams Favorites Awards Club 
--- p15 score=-9.356 id26401 ---
The Cisco Catalyst® 3550 Series Intelligent Ethernet Switches is a line of enterprise-class, stackable, multilayer switches that provide high availability, security and quality of 
--- p40 score=-8.401 id105391 ---
 Air Toxics Web Site The Air Pollution Prevention and Control Division of the U.S. EPAs National Risk Management Research Laboratory conducted a study to identify wood furniture ma
--- p65 score=-7.886 id81058 ---
<|endoftext|>Get updates from the newest expertise information and recent innovations in world. Find details about latest technology trends, information, current innovations in the
--- p85 score=-7.547 id101941 ---
 without<|endoftext|>In Subliminal XII (here) (Complements), I pictured myself as a man engaged in a lonely struggle attempting to expose subliminal practices in advertising. The t

[stdout]
valid docs 180129 of 182016
p 1: -11.044
p 5: -10.156
p10: -9.677
p25: -8.886
p50: -8.166
p75: -7.720
p90: -7.443
p99: -7.077
--- p2 score=-10.676 id119572 ---
 uk<|endoftext|>free chat - Shemalewebcams | Free Live Sex Chat For an ideal user experience please enable JavaScript in your web browser Settings! Live Cams Favorites Awards Club 
--- p15 score=-9.356 id26401 ---
The Cisco Catalyst® 3550 Series Intelligent Ethernet Switches is a line of enterprise-class, stackable, multilayer switches that provide high availability, security and quality of 
--- p40 score=-8.401 id105391 ---
 Air Toxics Web Site The Air Pollution Prevention and Control Division of the U.S. EPAs National Risk Management Research Laboratory conducted a study to identify wood furniture ma
--- p65 score=-7.886 id81058 ---
<|endoftext|>Get updates from the newest expertise information and recent innovations in world. Find details about latest technology trends, information, current innovations in the
--- p85 score=-7.547 id101941 ---
 without<|endoftext|>In Subliminal XII (here) (Complements), I pictured myself as a man engaged in a lonely struggle attempting to expose subliminal practices in advertising. The t
[metadata] {"noOutputExpected": false}
#29
narration

Key insight: fluency-max moved the wrong direction — it selected simple, predictable prose, while the target is information-rich (named entities, technical terms, code). And ratio-ranking got fooled by spam. The robust lever is likely removing junk (non-English, spam, boilerplate, number-lists) while preserving diversity. Let me build a heuristic junk filter and test filter-then-diverse against random.

toolWrite
args
{
  "file_path": "/tmp/filt.py",
  "content": "import json, re\nimport numpy as np\n\nWORD_RE = re.compile(r\"[A-Za-z]+\")\nALL_RE  = re.compile(r\"\\S+\")\n\ndef quality(text):\n    n=len(text)\n    if n<200: return None\n    ascii_letters=sum(1 for c in text if c.isascii() and c.isalpha())\n    ascii_ok=sum(1 for c in text if c.isascii())\n    letters=sum(1 for c in text if c.isalpha())\n    digits=sum(1 for c in text if c.isdigit())\n    if letters==0: return None\n    frac_ascii = ascii_ok/n\n    frac_nonlatin = 1 - (ascii_letters/max(1,letters))\n    toks=ALL_RE.findall(text)\n    words=WORD_RE.findall(text)\n    if len(toks)<40: return None\n    alpha_word_ratio=len(words)/len(toks)\n    mean_wlen=sum(len(w) for w in words)/max(1,len(words))\n    uniq=len(set(w.lower() for w in words))/max(1,len(words))\n    digit_ratio=digits/n\n    # fraction of alpha-words length>=3 (filters menu/list tokens)\n    return dict(n=n,frac_ascii=frac_ascii,frac_nonlatin=frac_nonlatin,\n                alpha_word_ratio=alpha_word_ratio,mean_wlen=mean_wlen,\n                uniq=uniq,digit_ratio=digit_ratio,nwords=len(words))\n\nids=[];texts=[]\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r=json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN=len(ids)\n\nkeep=[]\nreasons={}\nfor k in range(N):\n    q=quality(texts[k])\n    if q is None:\n        reasons['short']=reasons.get('short',0)+1; continue\n    bad=None\n    if q['frac_ascii']<0.85: bad='nonascii'\n    elif q['frac_nonlatin']>0.15: bad='nonlatin'\n    elif q['alpha_word_ratio']<0.6: bad='lowalpha'\n    elif q['mean_wlen']<3.2 or q['mean_wlen']>9: bad='wlen'\n    elif q['uniq']<0.28: bad='repetitive'\n    elif q['digit_ratio']>0.15: bad='digits'\n    if bad: reasons[bad]=reasons.get(bad,0)+1; continue\n    keep.append(k)\n\nest=sum(int(len(texts[k])/4) for k in keep)\nprint(\"kept\",len(keep),\"of\",N,\"est_tokens\",est)\nprint(\"reasons\",reasons)\nnp.save(\"/tmp/keep_idx.npy\",np.array(keep))\n"
}
Write result
File created successfully at: /tmp/filt.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/filt.py", "content": "import json, re\nimport numpy as np\n\nWORD_RE = re.compile(r\"[A-Za-z]+\")\nALL_RE  = re.compile(r\"\\S+\")\n\ndef quality(text):\n    n=len(text)\n    if n<200: return None\n    ascii_letters=sum(1 for c in text if c.isascii() and c.isalpha())\n    ascii_ok=sum(1 for c in text if c.isascii())\n    letters=sum(1 for c in text if c.isalpha())\n    digits=sum(1 for c in text if c.isdigit())\n    if letters==0: return None\n    frac_ascii = ascii_ok/n\n    frac_nonlatin = 1 - (ascii_letters/max(1,letters))\n    toks=ALL_RE.findall(text)\n    words=WORD_RE.findall(text)\n    if len(toks)<40: return None\n    alpha_word_ratio=len(words)/len(toks)\n    mean_wlen=sum(len(w) for w in words)/max(1,len(words))\n    uniq=len(set(w.lower() for w in words))/max(1,len(words))\n    digit_ratio=digits/n\n    # fraction of alpha-words length>=3 (filters menu/list tokens)\n    return dict(n=n,frac_ascii=frac_ascii,frac_nonlatin=frac_nonlatin,\n                alpha_word_ratio=alpha_word_ratio,mean_wlen=mean_wlen,\n                uniq=uniq,digit_ratio=digit_ratio,nwords=len(words))\n\nids=[];texts=[]\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r=json.loads(line); ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN=len(ids)\n\nkeep=[]\nreasons={}\nfor k in range(N):\n    q=quality(texts[k])\n    if q is None:\n        reasons['short']=reasons.get('short',0)+1; continue\n    bad=None\n    if q['frac_ascii']<0.85: bad='nonascii'\n    elif q['frac_nonlatin']>0.15: bad='nonlatin'\n    elif q['alpha_word_ratio']<0.6: bad='lowalpha'\n    elif q['mean_wlen']<3.2 or q['mean_wlen']>9: bad='wlen'\n    elif q['uniq']<0.28: bad='repetitive'\n    elif q['digit_ratio']>0.15: bad='digits'\n    if bad: reasons[bad]=reasons.get(bad,0)+1; continue\n    keep.append(k)\n\nest=sum(int(len(texts[k])/4) for k in keep)\nprint(\"kept\",len(keep),\"of\",N,\"est_tokens\",est)\nprint(\"reasons\",reasons)\nnp.save(\"/tmp/keep_idx.npy\",np.array(keep))\n", "structuredPatch": [], "originalFile": null, "userModified": false}
#30
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashApply heuristic junk filter and count survivors
args
{
  "command": "cd /workspace; python3 /tmp/filt.py",
  "description": "Apply heuristic junk filter and count survivors"
}
Bash result
Command did not complete within its 120s timeout and was moved to the background (ID: bjjklsob3). Output is being written to: /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bjjklsob3.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": "bjjklsob3", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#31
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait for filter result
args
{
  "command": "sleep 45; cat /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bjjklsob3.output 2>/dev/null; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Wait for filter result"
}
Bash result
kept 169670 of 182016 est_tokens 142238666
reasons {'repetitive': 6631, 'short': 2862, 'nonascii': 667, 'digits': 1018, 'nonlatin': 143, 'wlen': 257, 'lowalpha': 768}
---timer---
4318

[stdout]
kept 169670 of 182016 est_tokens 142238666
reasons {'repetitive': 6631, 'short': 2862, 'nonascii': 667, 'digits': 1018, 'nonlatin': 143, 'wlen': 257, 'lowalpha': 768}
---timer---
4318
[metadata] {"noOutputExpected": false}
#32
narration

The junk filter is light (kept 93%) — the pool is mostly English, so filtering alone ≈ random. The real lever is matching the 4 target registers while preserving within-register diversity = DSIR-style importance resampling (not top-k, which collapses diversity as fluency-max showed). Crucially, the junk filter removes exactly the artifacts (number-lists, repetitive galleries, foreign) that fooled the ratio scorer. Let me inspect the top gated docs under the ratio and bigram scores.

toolBashInspect top gated docs by ratio and bigram scores
args
{
  "command": "cd /workspace; python3 -c \"\nimport json,numpy as np\nkeep=set(np.load('/tmp/keep_idx.npy').tolist())\nids=np.load('/tmp/ids.npy')\ns_r1=np.load('/tmp/s_r1.npy'); s_big=np.load('/tmp/s_big.npy')\nmask=np.array([ (k in keep) for k in range(len(ids))])\ntext={}\nfor line in open('data/pool.jsonl'):\n    r=json.loads(line); text[r['id']]=r['text']\ndef show(name,sc):\n    s=sc.copy(); s[~mask]=-9e18\n    order=np.argsort(-s)[:8]\n    print('####',name)\n    for k in order:\n        print(f'[{sc[k]:.2f} id{ids[k]}]',text[int(ids[k])][:140].replace(chr(10),' '))\nshow('gated unigram_ratio',s_r1)\nshow('gated bigram_backoff',s_big)\n\"",
  "description": "Inspect top gated docs by ratio and bigram scores"
}
Bash result
#### gated unigram_ratio
[1.51 id165467] se Orchideeen Vereniging (1037) NOV alle >Home >Info >Gastenboek >Nieuw (recent geladen plaatjes) Acacallis Acineta Aerangis Aeranthes Aerid
[1.50 id146338] ? #debill What Digital Economy Bill? #debill Links @doc1online has declared that they do not recognise the Digital Economy Bill I choose not
[1.48 id123682]  Blogger.<|endoftext|>What Digital Economy Bill? #debill What Digital Economy Bill? #debill Links @doc1online has declared that they do not 
[1.26 id166511] .<|endoftext|>Russia, Asia - 1SmartList 0 messages Post Ad Russia Asia - All - Business Listings Cars, Vans & Motorbikes Community Electroni
[1.15 id135390] <|endoftext|>Team Band of Brothers - Last 100 Blog Posts - The World Race Resources Contact Us Catalog Request WR Newsletter Signup Training
[1.14 id158046]  2000 - 2019, TechTarget Close<|endoftext|>Team Band of Brothers - Last 100 Blog Posts - The World Race Resources Contact Us Catalog Request
[1.06 id161142] Spambot Killer Spambot Killer module for Nuke-Evolution Xtreme Nuke Evolution Xtreme - Leading Open Source Content Management System Taking 
[1.03 id158124] IAN BRAND - Asiatische Lebensmittel und Lebensart Aktuell Erweiterte Suche Chutney, Pickle, Papadams, Naan Essig, Öl, Ghee Fertiggerichte Fr
#### gated bigram_backoff
[-5.14 id32526] My dad has a green card and went back home over 2 years ago. He is now trying to come back after being away from the US for 2 years. I was t
[-5.57 id6656] Well, I made myself a blog I’ve been thinking of doing this for a while, largely as a journalling exercise for myself. I’m not writing for a
[-5.67 id69097]  you have come to this section of the website because you need to arrange a funeral then this is likely to be a difficult time for you and s
[-5.71 id97206] icator.<|endoftext|>The problem is, in hardware you can’t build a computer that’s twice as good as anyone else’s anymore. Too many people kn
[-5.72 id82898]  Best Bail Bond Agencies Near Us People who are caught that are committing a crime or have an arrest warrant would be apprehended. They woul
[-5.72 id84352]  of Having the Help of the Top Personal Injury Lawyer for Your Case The personal injury cases are one of the many types of the issues that y
[-5.72 id30387] <|endoftext|>I've been struggling with my calendar over the last few months. I know what I want, but it turns out that no one sells it. I am
[-5.74 id11940] You don’t know it yet, but we love you. You don’t know it yet, but we have been waiting for you. And you don’t know it yet, but you already

[stdout]
#### gated unigram_ratio
[1.51 id165467] se Orchideeen Vereniging (1037) NOV alle >Home >Info >Gastenboek >Nieuw (recent geladen plaatjes) Acacallis Acineta Aerangis Aeranthes Aerid
[1.50 id146338] ? #debill What Digital Economy Bill? #debill Links @doc1online has declared that they do not recognise the Digital Economy Bill I choose not
[1.48 id123682]  Blogger.<|endoftext|>What Digital Economy Bill? #debill What Digital Economy Bill? #debill Links @doc1online has declared that they do not 
[1.26 id166511] .<|endoftext|>Russia, Asia - 1SmartList 0 messages Post Ad Russia Asia - All - Business Listings Cars, Vans & Motorbikes Community Electroni
[1.15 id135390] <|endoftext|>Team Band of Brothers - Last 100 Blog Posts - The World Race Resources Contact Us Catalog Request WR Newsletter Signup Training
[1.14 id158046]  2000 - 2019, TechTarget Close<|endoftext|>Team Band of Brothers - Last 100 Blog Posts - The World Race Resources Contact Us Catalog Request
[1.06 id161142] Spambot Killer Spambot Killer module for Nuke-Evolution Xtreme Nuke Evolution Xtreme - Leading Open Source Content Management System Taking 
[1.03 id158124] IAN BRAND - Asiatische Lebensmittel und Lebensart Aktuell Erweiterte Suche Chutney, Pickle, Papadams, Naan Essig, Öl, Ghee Fertiggerichte Fr
#### gated bigram_backoff
[-5.14 id32526] My dad has a green card and went back home over 2 years ago. He is now trying to come back after being away from the US for 2 years. I was t
[-5.57 id6656] Well, I made myself a blog I’ve been thinking of doing this for a while, largely as a journalling exercise for myself. I’m not writing for a
[-5.67 id69097]  you have come to this section of the website because you need to arrange a funeral then this is likely to be a difficult time for you and s
[-5.71 id97206] icator.<|endoftext|>The problem is, in hardware you can’t build a computer that’s twice as good as anyone else’s anymore. Too many people kn
[-5.72 id82898]  Best Bail Bond Agencies Near Us People who are caught that are committing a crime or have an arrest warrant would be apprehended. They woul
[-5.72 id84352]  of Having the Help of the Top Personal Injury Lawyer for Your Case The personal injury cases are one of the many types of the issues that y
[-5.72 id30387] <|endoftext|>I've been struggling with my calendar over the last few months. I know what I want, but it turns out that no one sells it. I am
[-5.74 id11940] You don’t know it yet, but we love you. You don’t know it yet, but we have been waiting for you. And you don’t know it yet, but you already
[metadata] {"noOutputExpected": false}
#33
narration

Neither ratio (boilerplate) nor bigram (generic prose) cleanly isolates the target at the extremes, and top-k concentration hurt (610). The literature-correct move for distribution matching is DSIR importance resampling — sample proportional to the target/pool ratio so the aggregate set matches the target while individual diversity is preserved via Gumbel noise. I have per-doc ratio scores cached. Let me build a tunable resampler and test it against random.

toolWrite
args
{
  "file_path": "/tmp/resample.py",
  "content": "import json, numpy as np, sys\nT=float(sys.argv[1]) if len(sys.argv)>1 else 0.4\nOUT=sys.argv[2] if len(sys.argv)>2 else \"/tmp/sel_rs.json\"\ns=np.load(\"/tmp/s_r1.npy\")          # per-token unigram target/pool log-ratio\nids=np.load(\"/tmp/ids.npy\")\nkeep=np.load(\"/tmp/keep_idx.npy\")\nmask=np.zeros(len(ids),dtype=bool); mask[keep]=True\nmask &= (s>-8e8)\nidx=np.where(mask)[0]\nz=s[idx]\nz=(z-z.mean())/ (z.std()+1e-9)     # standardize\nrng=np.random.default_rng(0)\ng=rng.gumbel(size=len(idx))\nkey=z/T + g\norder=idx[np.argsort(-key)]\n# fill to 20M est tokens\ntext_len=np.load(\"/tmp/text_len.npy\")  # est tokens per doc, aligned to id index\nsel=[];cum=0\nfor k in order:\n    sel.append(int(ids[k])); cum+=int(text_len[k])\n    if cum>=20_000_000: break\njson.dump(sel,open(OUT,\"w\"))\nprint(\"T\",T,\"n\",len(sel),\"est\",cum)\n"
}
Write result
File created successfully at: /tmp/resample.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/resample.py", "content": "import json, numpy as np, sys\nT=float(sys.argv[1]) if len(sys.argv)>1 else 0.4\nOUT=sys.argv[2] if len(sys.argv)>2 else \"/tmp/sel_rs.json\"\ns=np.load(\"/tmp/s_r1.npy\")          # per-token unigram target/pool log-ratio\nids=np.load(\"/tmp/ids.npy\")\nkeep=np.load(\"/tmp/keep_idx.npy\")\nmask=np.zeros(len(ids),dtype=bool); mask[keep]=True\nmask &= (s>-8e8)\nidx=np.where(mask)[0]\nz=s[idx]\nz=(z-z.mean())/ (z.std()+1e-9)     # standardize\nrng=np.random.default_rng(0)\ng=rng.gumbel(size=len(idx))\nkey=z/T + g\norder=idx[np.argsort(-key)]\n# fill to 20M est tokens\ntext_len=np.load(\"/tmp/text_len.npy\")  # est tokens per doc, aligned to id index\nsel=[];cum=0\nfor k in order:\n    sel.append(int(ids[k])); cum+=int(text_len[k])\n    if cum>=20_000_000: break\njson.dump(sel,open(OUT,\"w\"))\nprint(\"T\",T,\"n\",len(sel),\"est\",cum)\n", "structuredPatch": [], "originalFile": null, "userModified": false}
#34
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashSave text lengths and build T=0.4 resample selection
args
{
  "command": "cd /workspace; python3 -c \"\nimport json,numpy as np\nids=np.load('/tmp/ids.npy'); tl=np.empty(len(ids),dtype=np.int64)\npos={}\ni=0\nfor line in open('data/pool.jsonl'):\n    r=json.loads(line); tl[i]=int(len(r['text'])/4); i+=1\nnp.save('/tmp/text_len.npy',tl); print('saved text_len',i)\n\"; python3 /tmp/resample.py 0.4 /tmp/sel_rs04.json",
  "description": "Save text lengths and build T=0.4 resample selection"
}
Bash result
saved text_len 182016
T 0.4 n 20102 est 20001414

[stdout]
saved text_len 182016
T 0.4 n 20102 est 20001414
[metadata] {"noOutputExpected": false}
#35
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashEval T=0.4 resample selection
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false; (bash dev_eval.sh /tmp/sel_rs04.json > /tmp/rs04.log 2>&1; echo DONE>>/tmp/rs04.log)",
  "description": "Eval T=0.4 resample selection",
  "run_in_background": true
}
Bash result
Command running in background with ID: bj9ly0cb1. Output is being written to: /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bj9ly0cb1.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": "bj9ly0cb1", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#36
narration

While T=0.4 trains, let me check timing and prepare a bigram-ratio variant. Let me wait for the result.

toolBashWait for T=0.4 eval result
args
{
  "command": "sleep 80; cat /tmp/rs04.log; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Wait for T=0.4 eval result"
}
Bash result
Token indices sequence length is longer than the specified maximum sequence length for this model (31886 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 20102 ids -> /tmp/dev_train.npy
---timer---
4087

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (31886 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 20102 ids -> /tmp/dev_train.npy
---timer---
4087
[metadata] {"noOutputExpected": false}
#37
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashWait for training to finish
args
{
  "command": "sleep 30; cat /tmp/rs04.log",
  "description": "Wait for training to finish"
}
Bash result
Token indices sequence length is longer than the specified maximum sequence length for this model (31886 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 20102 ids -> /tmp/dev_train.npy
HELDOUT_PPL 366.396  loss 5.9037  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
DONE

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (31886 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 20102 ids -> /tmp/dev_train.npy
HELDOUT_PPL 366.396  loss 5.9037  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
DONE
[metadata] {"noOutputExpected": false}
#38
narration

PPL 366 — beats random (458) substantially! DSIR resampling with preserved diversity is the right approach. Now let me tune the tilt temperature. Let me bracket with a stronger tilt (T=0.25) and weaker (T=0.6).

toolBashBuild resample selections for T=0.25, 0.6, 0.15
args
{
  "command": "cd /workspace; python3 /tmp/resample.py 0.25 /tmp/sel_rs025.json; python3 /tmp/resample.py 0.6 /tmp/sel_rs06.json; python3 /tmp/resample.py 0.15 /tmp/sel_rs015.json",
  "description": "Build resample selections for T=0.25, 0.6, 0.15"
}
Bash result
T 0.25 n 19796 est 20003959
T 0.6 n 20363 est 20002277
T 0.15 n 19623 est 20000600

[stdout]
T 0.25 n 19796 est 20003959
T 0.6 n 20363 est 20002277
T 0.15 n 19623 est 20000600
[metadata] {"noOutputExpected": false}
#39
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashEval T=0.25, 0.6, 0.15 sequentially
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false\nfor t in 025 06 015; do\n  bash dev_eval.sh /tmp/sel_rs${t}.json > /tmp/rs${t}.log 2>&1\n  echo \"T=$t : $(grep HELDOUT /tmp/rs${t}.log)\"\ndone\necho ALLDONE",
  "description": "Eval T=0.25, 0.6, 0.15 sequentially",
  "run_in_background": true
}
Bash result
Command running in background with ID: bz3ypk0p9. Output is being written to: /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.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": "bz3ypk0p9", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#40
narration

Three temperatures evaluating (~5 min). Meanwhile, let me compute a stronger uni+bigram ratio signal in the background (CPU, won't contend with GPU) to potentially beat the unigram-only signal.

toolWrite
args
{
  "file_path": "/tmp/bir.py",
  "content": "import json, re, numpy as np\nfrom math import log\nWORD_RE=re.compile(r\"[a-z0-9]+\"); CAP=1200\ndef toks(t):\n    w=WORD_RE.findall(t.lower()); return w[:CAP] if len(w)>CAP else w\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained(\"gpt2\")\ndev=np.load(\"/workspace/data/multi_dev.npy\")\ntgt=[d for d in tok.decode(dev.tolist()).split(\"<|endoftext|>\") if d.strip()]\ndef counts(docs):\n    u={};b={};U=0;B=0\n    for d in docs:\n        w=toks(d)\n        for x in w:u[x]=u.get(x,0)+1;U+=1\n        for i in range(len(w)-1):\n            k=w[i]+\" \"+w[i+1];b[k]=b.get(k,0)+1;B+=1\n    return u,b,U,B\ntu,tb,tU,tB=counts(tgt)\nids=[];texts=[]\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r=json.loads(line);ids.append(r[\"id\"]);texts.append(r[\"text\"])\nN=len(ids)\nrng=np.random.default_rng(1337)\nsamp=[texts[j] for j in rng.choice(N,size=25000,replace=False)]\nbu,bb,bU,bB=counts(samp)\nSM=1.0\nVu=len(set(tu)|set(bu)); Vb=len(set(tb)|set(bb))\ndtu=tU+SM*Vu; dbu=bU+SM*Vu; dtb=tB+SM*Vb; dbb=bB+SM*Vb\nwu={f: log((tu.get(f,0)+SM)/dtu)-log((bu.get(f,0)+SM)/dbu) for f in set(tu)|set(bu)}\nwb={f: log((tb.get(f,0)+SM)/dtb)-log((bb.get(f,0)+SM)/dbb) for f in set(tb)|set(bb)}\nUunseen=log(SM/dtu)-log(SM/dbu); Bunseen=log(SM/dtb)-log(SM/dbb)\ngu=wu.get; gb=wb.get\ns=np.full(N,-9e9)\nfor k in range(N):\n    w=toks(texts[k])\n    if len(w)<25: continue\n    ss=0.0;n=0\n    for x in w: ss+=gu(x,Uunseen); n+=1\n    for i in range(len(w)-1): ss+=gb(w[i]+\" \"+w[i+1],Bunseen); n+=1\n    s[k]=ss/n\nnp.save(\"/tmp/s_bir.npy\",s)\nprint(\"done bir\")\n"
}
Write result
File created successfully at: /tmp/bir.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/bir.py", "content": "import json, re, numpy as np\nfrom math import log\nWORD_RE=re.compile(r\"[a-z0-9]+\"); CAP=1200\ndef toks(t):\n    w=WORD_RE.findall(t.lower()); return w[:CAP] if len(w)>CAP else w\nfrom transformers import AutoTokenizer\ntok=AutoTokenizer.from_pretrained(\"gpt2\")\ndev=np.load(\"/workspace/data/multi_dev.npy\")\ntgt=[d for d in tok.decode(dev.tolist()).split(\"<|endoftext|>\") if d.strip()]\ndef counts(docs):\n    u={};b={};U=0;B=0\n    for d in docs:\n        w=toks(d)\n        for x in w:u[x]=u.get(x,0)+1;U+=1\n        for i in range(len(w)-1):\n            k=w[i]+\" \"+w[i+1];b[k]=b.get(k,0)+1;B+=1\n    return u,b,U,B\ntu,tb,tU,tB=counts(tgt)\nids=[];texts=[]\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r=json.loads(line);ids.append(r[\"id\"]);texts.append(r[\"text\"])\nN=len(ids)\nrng=np.random.default_rng(1337)\nsamp=[texts[j] for j in rng.choice(N,size=25000,replace=False)]\nbu,bb,bU,bB=counts(samp)\nSM=1.0\nVu=len(set(tu)|set(bu)); Vb=len(set(tb)|set(bb))\ndtu=tU+SM*Vu; dbu=bU+SM*Vu; dtb=tB+SM*Vb; dbb=bB+SM*Vb\nwu={f: log((tu.get(f,0)+SM)/dtu)-log((bu.get(f,0)+SM)/dbu) for f in set(tu)|set(bu)}\nwb={f: log((tb.get(f,0)+SM)/dtb)-log((bb.get(f,0)+SM)/dbb) for f in set(tb)|set(bb)}\nUunseen=log(SM/dtu)-log(SM/dbu); Bunseen=log(SM/dtb)-log(SM/dbb)\ngu=wu.get; gb=wb.get\ns=np.full(N,-9e9)\nfor k in range(N):\n    w=toks(texts[k])\n    if len(w)<25: continue\n    ss=0.0;n=0\n    for x in w: ss+=gu(x,Uunseen); n+=1\n    for i in range(len(w)-1): ss+=gb(w[i]+\" \"+w[i+1],Bunseen); n+=1\n    s[k]=ss/n\nnp.save(\"/tmp/s_bir.npy\",s)\nprint(\"done bir\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}
#41
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCompute uni+bigram ratio score in background
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false; (python3 /tmp/bir.py > /tmp/bir.out 2>&1; echo DONE>>/tmp/bir.out) ",
  "description": "Compute uni+bigram ratio score in background",
  "run_in_background": true
}
Bash result
Command running in background with ID: b3fhu0gvb. Output is being written to: /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/b3fhu0gvb.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": "b3fhu0gvb", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#42
narration

Let me wait for the temperature sweep to complete.

toolBashCheck temp sweep and bir progress
args
{
  "command": "sleep 150; echo \"=== temp sweep ===\"; cat /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.output 2>/dev/null | grep -E \"T=|HELDOUT\"; echo \"=== bir ===\"; cat /tmp/bir.out 2>/dev/null; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Check temp sweep and bir progress"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure
#43
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck sweep and bir results
args
{
  "command": "echo \"=== temp sweep ===\"; cat /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.output 2>/dev/null | grep -E \"T=|HELDOUT\"; echo \"=== bir ===\"; cat /tmp/bir.out; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Check sweep and bir results"
}
Bash result
=== temp sweep ===
T=025 : HELDOUT_PPL 372.510  loss 5.9203  train_s 55  /tmp/dev_train.npy
=== bir ===
done bir
DONE
---timer---
3876

[stdout]
=== temp sweep ===
T=025 : HELDOUT_PPL 372.510  loss 5.9203  train_s 55  /tmp/dev_train.npy
=== bir ===
done bir
DONE
---timer---
3876
[metadata] {"noOutputExpected": false}
#44
narration

T=0.25 → 372.5 (vs T=0.4 → 366). Optimum near 0.4. Let me wait for T=0.6 and T=0.15.

toolBashWait for remaining temp sweep evals
args
{
  "command": "sleep 120; cat /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.output 2>/dev/null | grep -E \"T=|ALLDONE\"; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Wait for remaining temp sweep evals"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure
#45
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck sweep results
args
{
  "command": "cat /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.output 2>/dev/null | grep -E \"T=|ALLDONE\"; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Check sweep results"
}
Bash result
T=025 : HELDOUT_PPL 372.510  loss 5.9203  train_s 55  /tmp/dev_train.npy
T=06 : HELDOUT_PPL 375.067  loss 5.9271  train_s 56  /tmp/dev_train.npy
---timer---
3746

[stdout]
T=025 : HELDOUT_PPL 372.510  loss 5.9203  train_s 55  /tmp/dev_train.npy
T=06 : HELDOUT_PPL 375.067  loss 5.9271  train_s 56  /tmp/dev_train.npy
---timer---
3746
[metadata] {"noOutputExpected": false}
#46
narration

T=0.6 → 375. Curve: 0.25→372.5, 0.4→366, 0.6→375. Optimum near 0.4. Now let me test the stronger uni+bigram ratio signal. Let me generalize the resampler and wait for T=0.15.

toolWrite
args
{
  "file_path": "/tmp/resample2.py",
  "content": "import json, numpy as np, sys\nSCORE=sys.argv[1]; T=float(sys.argv[2]); OUT=sys.argv[3]\ns=np.load(SCORE); ids=np.load(\"/tmp/ids.npy\")\nkeep=np.load(\"/tmp/keep_idx.npy\")\nmask=np.zeros(len(ids),dtype=bool); mask[keep]=True\nmask &= (s>-8e8)\nidx=np.where(mask)[0]\nz=s[idx]; z=(z-z.mean())/(z.std()+1e-9)\nrng=np.random.default_rng(0)\nkey=z/T + rng.gumbel(size=len(idx))\norder=idx[np.argsort(-key)]\ntl=np.load(\"/tmp/text_len.npy\")\nsel=[];cum=0\nfor k in order:\n    sel.append(int(ids[k])); cum+=int(tl[k])\n    if cum>=20_000_000: break\njson.dump(sel,open(OUT,\"w\"))\nprint(SCORE,\"T\",T,\"n\",len(sel),\"est\",cum)\n"
}
Write result
File created successfully at: /tmp/resample2.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/tmp/resample2.py", "content": "import json, numpy as np, sys\nSCORE=sys.argv[1]; T=float(sys.argv[2]); OUT=sys.argv[3]\ns=np.load(SCORE); ids=np.load(\"/tmp/ids.npy\")\nkeep=np.load(\"/tmp/keep_idx.npy\")\nmask=np.zeros(len(ids),dtype=bool); mask[keep]=True\nmask &= (s>-8e8)\nidx=np.where(mask)[0]\nz=s[idx]; z=(z-z.mean())/(z.std()+1e-9)\nrng=np.random.default_rng(0)\nkey=z/T + rng.gumbel(size=len(idx))\norder=idx[np.argsort(-key)]\ntl=np.load(\"/tmp/text_len.npy\")\nsel=[];cum=0\nfor k in order:\n    sel.append(int(ids[k])); cum+=int(tl[k])\n    if cum>=20_000_000: break\njson.dump(sel,open(OUT,\"w\"))\nprint(SCORE,\"T\",T,\"n\",len(sel),\"est\",cum)\n", "structuredPatch": [], "originalFile": null, "userModified": false}
#47
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashBuild bigram-ratio selections; check T=0.15
args
{
  "command": "cd /workspace\npython3 /tmp/resample2.py /tmp/s_bir.npy 0.4 /tmp/sel_bir04.json\npython3 /tmp/resample2.py /tmp/s_bir.npy 0.5 /tmp/sel_bir05.json\ncat /tmp/rs015.log 2>/dev/null | grep HELDOUT || echo \"015 not done yet\"",
  "description": "Build bigram-ratio selections; check T=0.15"
}
Bash result
/tmp/s_bir.npy T 0.4 n 19074 est 20000239
/tmp/s_bir.npy T 0.5 n 19238 est 20000903
015 not done yet

[stdout]
/tmp/s_bir.npy T 0.4 n 19074 est 20000239
/tmp/s_bir.npy T 0.5 n 19238 est 20000903
015 not done yet
[metadata] {"noOutputExpected": false}
#48
reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBashCheck if T=0.15 done
args
{
  "command": "sleep 20; grep -E \"T=015|ALLDONE\" /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/bz3ypk0p9.output 2>/dev/null; grep HELDOUT /tmp/rs015.log 2>/dev/null || echo \"015 still running\"",
  "description": "Check if T=0.15 done"
}
Bash result
T=015 : HELDOUT_PPL 368.997  loss 5.9108  train_s 55  /tmp/dev_train.npy
ALLDONE
HELDOUT_PPL 368.997  loss 5.9108  train_s 55  /tmp/dev_train.npy

[stdout]
T=015 : HELDOUT_PPL 368.997  loss 5.9108  train_s 55  /tmp/dev_train.npy
ALLDONE
HELDOUT_PPL 368.997  loss 5.9108  train_s 55  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}
#49
narration

Unigram-ratio curve is flat, min at T=0.4 (366). Now the key test — does the uni+bigram signal beat unigram-only? Let me eval bir at T=0.4 and 0.5.

toolBashEval uni+bigram ratio at T=0.4 and 0.5
args
{
  "command": "cd /workspace; export TOKENIZERS_PARALLELISM=false\nfor f in bir04 bir05; do\n  bash dev_eval.sh /tmp/sel_${f}.json > /tmp/${f}.log 2>&1\n  echo \"$f : $(grep HELDOUT /tmp/${f}.log)\"\ndone; echo ALLDONE",
  "description": "Eval uni+bigram ratio at T=0.4 and 0.5",
  "run_in_background": true
}
Bash result
Command running in background with ID: b3691i9ph. Output is being written to: /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/b3691i9ph.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": "b3691i9ph", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}
#50
narration

Let me wait for the uni+bigram results.

toolBashCheck bir eval results
args
{
  "command": "sleep 150; grep -E \"bir0|ALLDONE\" /tmp/claude-0/-workspace/15a2b8ef-4272-42e8-a818-f020ab1f9107/tasks/b3691i9ph.output 2>/dev/null; echo \"---timer---\"; cat /workspace/.timer/remaining_secs",
  "description": "Check bir eval results"
}
Bash result · error
Exit code 143
Command timed out after 2m 0s

[error] tool reported failure