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: 102
duration: 1h 9m
recorded: 2026-08-04

run iddclm-nano-curation__TGPvq9r

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

download JSON ↗

claude-codeclaude-opus-4-884 turns55 narration102 action102 observation29 reasoning1 final

#76

narration

No task-spec file exists (paths come from the task description: selection.json in submission/, curate.py + claim.md in /workspace/). Let me lock in the known-good deliverables first (copy into submission/ as a hedge + safety), verify no leftover placeholders, then use remaining time for one more principled experiment.

toolBash

args

{
  "command": "cd /workspace\n# hedge: also place script+claim in submission/ bundle (harmless; paths inside are absolute)\ncp curate.py submission/curate.py\ncp claim.md submission/claim.md\necho \"=== leftover placeholders in claim.md? ===\"; grep -nE '\\{[A-Z_]+\\}' claim.md || echo \"none\"\necho \"=== claim.md sections ===\"; grep -nE '^##' claim.md\necho \"=== selection.json final validity ===\"\npython3 -c \"\nimport json\ns=json.load(open('submission/selection.json'))\nassert all(isinstance(x,int) for x in s), 'non-int'\nassert len(s)==len(set(s)), 'dupes'\nassert min(s)>=0 and max(s)<182016, 'range'\nprint('OK: %d unique ids, range [%d,%d]'%(len(s),min(s),max(s)))\n\""
}

Bash result

=== leftover placeholders in claim.md? ===
none
=== claim.md sections ===
3:## Hypothesis
16:## Mechanism — a prediction that is NOT the final perplexity
21:### (O1) Data-level cross-entropy, before any model is trained — MEASURED
37:### (O2) The trained model's gain is register-localized — MEASURED
55:## Falsification
69:## Transfer
=== selection.json final validity ===
OK: 45000 unique ids, range [1,181981]

[stdout]
=== leftover placeholders in claim.md? ===
none
=== claim.md sections ===
3:## Hypothesis
16:## Mechanism — a prediction that is NOT the final perplexity
21:### (O1) Data-level cross-entropy, before any model is trained — MEASURED
37:### (O2) The trained model's gain is register-localized — MEASURED
55:## Falsification
69:## Transfer
=== selection.json final validity ===
OK: 45000 unique ids, range [1,181981]
[metadata] {"noOutputExpected": false}

#77

narration

All deliverables locked in submission/ and valid; claim.md complete with all four sections and measured O1/O2. With ~30 min left, one more principled experiment: the O2 result shows code is a fixed loss and prose is 75% of the eval. My head currently uses prose_df + tech_df as positives — the tech_df adds code-token weight that's unmatchable in the pool. Let me test a prose-only head (optimize purely for the matchable 75%). This is additive (flag, default off), so the shipped version stays safe.

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/curate.py",
  "old_string": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)",
  "new_string": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)"
}

Edit result

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": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)", "newString": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)", "originalFile": null, "structuredPatch": [{"oldStart": 238, "oldLines": 7, "newStart": 238, "newLines": 10, "lines": [" ", "     # single COMBINED head: all dev registers as positives (pool has no code to", "     # justify a separate technical selector -- see module docstring)", "-    r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)", "+    if \"--prose-only\" in sys.argv:", "+        r_all = nb_logodds(prose_df, bg_df, npp, nbg)", "+    else:", "+        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)", "     score = mean_score(r_all, allidx, indptr)", " ", "     nw, tok_est, alpha, digit, ascii_, sym, stopfrac, uniq, mwl, fshort, has_code = \\"]}], "userModified": false, "replaceAll": false}

#78

narration

Let me back up the known-good selection, generate the prose-only variant, and eval it:

toolBash

args

{
  "command": "cd /workspace\ncp submission/selection.json /tmp/sel_good.json\ntimeout 120 python3 curate.py --prose-only 2>&1 | grep -v -i warning | grep -E \"candidates|selected|wrote\"\n(bash dev_eval.sh /workspace/submission/selection.json > /tmp/eval_prose.log 2>&1; echo \"DONE $?\" >> /tmp/eval_prose.log) &\necho \"launched prose-only eval; timer=$(cat /workspace/.timer/remaining_secs)s\""
}

Bash result

[2] candidates: 107423 pass filter (dropped 74593)
    selected 45000 docs (~52M est tokens, best-first)
[3] wrote 45000 ids -> /workspace/submission/selection.json (~52M est tokens) in 3s
launched prose-only eval; timer=1725s

[stdout]
[2] candidates: 107423 pass filter (dropped 74593)
    selected 45000 docs (~52M est tokens, best-first)
[3] wrote 45000 ids -> /workspace/submission/selection.json (~52M est tokens) in 3s
launched prose-only eval; timer=1725s
[metadata] {"noOutputExpected": false}

#79

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolBash

args

{
  "command": "sleep 95; cat /tmp/eval_prose.log 2>/dev/null | grep -E \"HELDOUT|DONE\""
}

Bash result

HELDOUT_PPL 440.905  loss 6.0888  train_s 54  /tmp/dev_train.npy
DONE 0

[stdout]
HELDOUT_PPL 440.905  loss 6.0888  train_s 54  /tmp/dev_train.npy
DONE 0
[metadata] {"noOutputExpected": false}

#80

narration

Prose-only head: 440.905 — much worse (+14.5%). Dropping the tech_df positives badly hurts: the Q&A/technical vocabulary they contribute is important for matching informational prose in the broad target. This empirically validates the combined-head design. Restore the best selection and record the ablation:

toolBash

args

{
  "command": "cd /workspace\ncp /tmp/sel_good.json submission/selection.json\npython3 -c \"\nimport json; s=json.load(open('submission/selection.json'))\nassert len(s)==len(set(s))==45000 and min(s)>=0 and max(s)<182016\nprint('restored best selection: %d unique ids OK'%len(s))\n\""
}

Bash result

restored best selection: 45000 unique ids OK

[stdout]
restored best selection: 45000 unique ids OK
[metadata] {"noOutputExpected": false}

#81

reasoning (redacted)

encrypted chain-of-thought — recorded, not exposed

toolEdit

args

{
  "replace_all": false,
  "file_path": "/workspace/curate.py",
  "old_string": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)",
  "new_string": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring). ABLATION\n    # (--prose-only) drops the Q&A positives; empirically that WORSENS dev PPL\n    # (440.9 vs 384.965), i.e. folding the Q&A questions' technical vocabulary into\n    # the log-odds genuinely helps select informational prose. Default = combined.\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)"
}

Edit result

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": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring)\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)", "newString": "    # single COMBINED head: all dev registers as positives (pool has no code to\n    # justify a separate technical selector -- see module docstring). ABLATION\n    # (--prose-only) drops the Q&A positives; empirically that WORSENS dev PPL\n    # (440.9 vs 384.965), i.e. folding the Q&A questions' technical vocabulary into\n    # the log-odds genuinely helps select informational prose. Default = combined.\n    if \"--prose-only\" in sys.argv:\n        r_all = nb_logodds(prose_df, bg_df, npp, nbg)\n    else:\n        r_all = nb_logodds(prose_df + tech_df, bg_df, npp + npt, nbg)\n    score = mean_score(r_all, allidx, indptr)", "originalFile": null, "structuredPatch": [{"oldStart": 237, "oldLines": 7, "newStart": 237, "newLines": 10, "lines": ["     print(f\"    N={n} feats in {time.time()-t0:.0f}s\", flush=True)", " ", "     # single COMBINED head: all dev registers as positives (pool has no code to", "-    # justify a separate technical selector -- see module docstring)", "+    # justify a separate technical selector -- see module docstring). ABLATION", "+    # (--prose-only) drops the Q&A positives; empirically that WORSENS dev PPL", "+    # (440.9 vs 384.965), i.e. folding the Q&A questions' technical vocabulary into", "+    # the log-odds genuinely helps select informational prose. Default = combined.", "     if \"--prose-only\" in sys.argv:", "         r_all = nb_logodds(prose_df, bg_df, npp, nbg)", "     else:"]}], "userModified": false, "replaceAll": false}

#82

narration

Now update the submission/ copy and verify curate.py default deterministically reproduces the shipped selection.json (reproducibility is a hard requirement):

toolBash

args

{
  "command": "cd /workspace\ncp curate.py submission/curate.py\ncp claim.md submission/claim.md\n# reproducibility check: default run must equal the shipped selection\ntimeout 120 python3 curate.py 2>&1 | grep -E \"wrote\" \npython3 -c \"\nimport json\na=json.load(open('submission/selection.json')); b=json.load(open('/tmp/sel_good.json'))\nprint('reproducible (identical to validated selection):', a==b)\n\""
}

Bash result

[3] wrote 45000 ids -> /workspace/submission/selection.json (~52M est tokens) in 3s
reproducible (identical to validated selection): True

[stdout]
[3] wrote 45000 ids -> /workspace/submission/selection.json (~52M est tokens) in 3s
reproducible (identical to validated selection): True
[metadata] {"noOutputExpected": false}

#83

narration

Reproducibility confirmed — curate.py default deterministically regenerates the exact validated selection. Final verification pass:

toolBash

args

{
  "command": "cd /workspace\necho \"=== curate.py compiles ===\"; python3 -m py_compile curate.py && echo \"OK\"\necho \"=== deliverables ===\"; ls -la submission/selection.json curate.py claim.md submission/curate.py submission/claim.md\necho \"=== final packer coverage check (>=12M required) ===\"\npython3 pack_selection.py submission/selection.json /tmp/verify.npy 2>&1 | grep -i packed\npython3 -c \"import numpy as np; print('packed tokens:', len(np.load('/tmp/verify.npy')), '>= 12M:', len(np.load('/tmp/verify.npy'))>=12_000_000)\"\necho \"timer=$(cat /workspace/.timer/remaining_secs)s\""
}

Bash result

=== curate.py compiles ===
OK
=== deliverables ===
-rw-r--r-- 1 root root   5342 Aug  4 04:22 claim.md
-rw-r--r-- 1 root root  15417 Aug  4 04:26 curate.py
-rw-r--r-- 1 root root   5342 Aug  4 04:26 submission/claim.md
-rw-r--r-- 1 root root  15417 Aug  4 04:26 submission/curate.py
-rw-r--r-- 1 root root 317551 Aug  4 04:26 submission/selection.json
=== final packer coverage check (>=12M required) ===
packed 12000000 tokens from 45000 ids -> /tmp/verify.npy
packed tokens: 12000000 >= 12M: True
timer=1515s

[stdout]
=== curate.py compiles ===
OK
=== deliverables ===
-rw-r--r-- 1 root root   5342 Aug  4 04:22 claim.md
-rw-r--r-- 1 root root  15417 Aug  4 04:26 curate.py
-rw-r--r-- 1 root root   5342 Aug  4 04:26 submission/claim.md
-rw-r--r-- 1 root root  15417 Aug  4 04:26 submission/curate.py
-rw-r--r-- 1 root root 317551 Aug  4 04:26 submission/selection.json
=== final packer coverage check (>=12M required) ===
packed 12000000 tokens from 45000 ids -> /tmp/verify.npy
packed tokens: 12000000 >= 12M: True
timer=1515s
[metadata] {"noOutputExpected": false}

#84

narration

All deliverables verified and complete. Let me finalize the task list:

toolTaskUpdate

args

{
  "taskId": "4",
  "status": "completed"
}

TaskUpdate result

Updated task #4 status

[metadata] {"success": true, "taskId": "4", "updatedFields": ["status"], "statusChange": {"from": "in_progress", "to": "completed"}}

toolTaskUpdate

args

{
  "taskId": "5",
  "status": "completed"
}

TaskUpdate result

Updated task #5 status

[metadata] {"success": true, "taskId": "5", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "completed"}}

final summary

Done. All three deliverables are complete, validated by real training runs, and reproducible.

Result

Curated selection scores 384.97 held-out perplexity vs. 476.71 random baseline — a 19.2% reduction.

What's shipped

File	Contents
`/workspace/submission/selection.json`	45,000 unique in-range ids, priority-ordered; packer fills exactly 12M tokens with reserve
`/workspace/curate.py`	Self-contained, deterministic; default run reproduces `selection.json` bit-for-bit
`/workspace/claim.md`	Hypothesis / Mechanism / Falsification / Transfer, with measured O1 + O2

(Copies of curate.py and claim.md also placed in submission/ as a path hedge.)

The criterion (stated in `curate.py`)

Rank pool docs by a DSIR/NBSVM log-likelihood-ratio to the disclosed target (log P(f|target) − log P(f|background) over hashed unigram+bigram features, scored as the mean of clipped per-feature weights — kills length bias and rare-word-salad), behind an English clean-prose junk filter (stopword-fraction, mean-word-length, unique-ratio, ascii/symbol/digit gates), deduplicated by content fingerprint. A single combined head (all dev registers as positives) because the pool audit found no code — a separate technical selector only drags in multilingual boilerplate.

Experiments run (the eval was the oracle)

Combined head + filter: 384.97 ← best, shipped
+ word-bigram fluency demotion: 385.9 (neutral — filter already removes most junk)
Force ~25% encyclopedic: 396.1 (worse — pool under-supplies wiki)
Prose-only head (drop Q&A positives): 440.9 (worse — Q&A vocab matters)

Mechanism, measured (the interesting part)

The 19% win is register-localized, not uniform. Splitting the target into prose (75%) and code/Q&A (25%):

O1 (data cross-entropy, no model): prose 8.128→7.889 (↓), code 8.068→8.447 (↑)
O2 (per-register perplexity): prose 519→360 (−31%), code 370→469 (+27%)

Because the pool has no code, curation doesn't just fail to help the code register — it trades it away to buy prose. That's a falsifiable, structured prediction distinct from the single aggregate number, and it's what the claim rests on.

~25 min of budget remain, but further tuning would risk overfitting the dev proxy against the hidden official target — the four controlled ablations agree the combined head is robustly best, so I stopped there.

Result

What's shipped

The criterion (stated in curate.py)

Experiments run (the eval was the oracle)

Mechanism, measured (the interesting part)

The criterion (stated in `curate.py`)